Question: Facial detection through the merging of quantized skin color regions and wavelet packet analysis.

This document presents a face recognition system that utilizes probabilistic decision-based neural networks (PDBNN), capitalizing on advancements in microelectronics and vision systems to make high-performance biometric recognition techniques economically viable. Face recognition, in particular, has garnered significant interest due to its potential for non-intrusiveness and user-friendliness among biometric identification methods. The proposed PDBNN-based system is comprised of three main components: a face detector that identifies the location of a human face within an image, an eye localizer that determines the positions of both eyes for generating relevant feature vectors, and a face recognizer. The facial region analyzed includes eyebrows, eyes, and the nose, while the mouth is excluded (eye-glasses are permitted). The PDBNN is effectively deployed across all three modules, featuring a hierarchical network structure with nonlinear basis functions and a competitive credit-assignment mechanism. The efficacy of the PDBNN in face recognition is demonstrated through its successful application on three databases: two public (FERET and ORL) and one in-house (SCR). Performance metrics, including recognition accuracy and false rejection and acceptance rates, are detailed for each database. In terms of processing speed, the entire recognition process, encompassing PDBNN operations for eye localization, feature extraction, and classification, takes approximately one second on a Sparc10 system, without the use of hardware accelerators or co-processors.

A novel face detection technique combining quantized skin color regions merging and wavelet packet analysis is introduced in this paper. This method leverages the distinctiveness of skin color in quantized color spaces to identify potential face regions, followed by a merging process to consolidate these regions into coherent face candidates. Wavelet packet analysis is then applied to refine the detection by analyzing the texture and spatial structure of the identified regions. This two-step approach ensures robustness against varying lighting conditions and partial occlusions. The quantization of skin color reduces the computational complexity, making it suitable for real-time applications, while the wavelet packet analysis enhances the accuracy of face localization. The proposed technique is validated on a diverse set of images, demonstrating its effectiveness in accurately detecting faces under various conditions. The results show a high detection rate with a low false positive rate, positioning this method as a promising solution for face detection in practical scenarios.

Pedestrian detection will play a major role in future driver assistance and autonomous driving. One powerful algorithm in this field uses HOG features to describe the specific properties of pedestrians in images. To determine their locations, features are extracted and classified window-wise from different scales of an input image. The results of the classification are finally merged to remove overlapping detections. The real-time execution of this method requires specific FPGA- or ASIC-architectures. Recent work focused on accelerating the feature extraction and classification. Although merging is an important step in the algorithm, it is only rarely considered in hardware implementations. A reason for that could be its complexity and irregularity that is not trivial to implement in hardware. In this paper, we present a new bottom-up FPGA architecture that maps the full HOG-based algorithm for pedestrian detection including feature extraction, SVM classification, and multi-scale processing in combination with merging. For that purpose, we also propose a new hardware-optimized merging method. The resulting architecture is highly efficient. Additionally, we present an FPGA-based full real-time and multi-scale pedestrian detection demonstration system.

Parameterized models can produce realistic, manipulable images of human faces— with a surprisingly small number of parameters.

In this paper, we introduce the DEEP-SEE FACE framework, an assistive device designed to improve cognition, interaction, and communication of visually impaired (VI) people in social encounters. The proposed approach jointly exploits computer vision algorithms (region proposal networks, ATLAS tracking and global, and low-level image descriptors) and deep convolutional neural networks in order to detect, track, and recognize, in real-time, various persons existent in the video streams. The major contribution of the paper concerns a global, fixed-size face representation that takes into the account of various video frames while remaining independent of the length of the image sequence. To this purpose, we introduce an effective weight adaptation scheme that is able to determine the relevance assigned to each face instance, depending on the frame degree of motion/camera blur, scale variation, and compression artifacts. Another relevant contribution involves a hard negative mining stage that helps us differentiating between known and unknown face identities. The experimental results, carried out on a large-scale data set, validate the proposed methodology with an average accuracy and recognition rates superior to 92%. When tested in real life, indoor/outdoor scenarios, the DEEP-SEE FACE prototype proves to be effective and easy to use, allowing the VI people to access visual information during social events.

Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon's entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed.

Designing systems able to interact with students in a natural manner is a complex and far from solved problem. A key aspect of natural interaction is the ability to understand and appropriately respond to human emotions. This paper details our response to the continuous Audio/Visual Emotion Challenge (AVEC'12) whose goal is to predict four affective signals describing human emotions. The proposed method uses Fourier spectra to extract multi-scale dynamic descriptions of signals characterizing face appearance, head movements and voice. We perform a kernel regression with very few representative samples selected via a supervised weighted-distance-based clustering, that leads to a high generalization power. We also propose a particularly fast regressor-level fusion framework to merge systems based on different modalities. Experiments have proven the efficiency of each key point of the proposed method and our results on challenge data were the highest among 10 international research teams. Key-words Affective computing, Dynamic features, Multimodal fusion, Feature selection, Facial expressions. ACM Classification

As one of the most successful applications of image analysis and understanding, face recognition has recently received significant attention, especially during the past several years. At least two reasons account for this trend: the first is the wide range of commercial and law enforcement applications, and the second is the availability of feasible technologies after 30 years of research. Even though current machine recognition systems have reached a certain level of maturity, their success is limited by the conditions imposed by many real applications. For example, recognition of face images acquired in an outdoor environment with changes in illumination and/or pose remains a largely unsolved problem. In other words, current systems are still far away from the capability of the human perception system.This paper provides an up-to-date critical survey of still- and video-based face recognition research. There are two underlying motivations for us to write this survey paper: the first is to provide an up-to-date review of the existing literature, and the second is to offer some insights into the studies of machine recognition of faces. To provide a comprehensive survey, we not only categorize existing recognition techniques but also present detailed descriptions of representative methods within each category. In addition, relevant topics such as psychophysical studies, system evaluation, and issues of illumination and pose variation are covered.

Stochastic modelling of non-stationary vector timeseries based on HMMs has been very successful for speech applications [5]. Recently it has been applied to a range of image recognition problems [7, 9]. Previously reported work [6] has investigated the use of HMMs to model human faces for identi cation purposes. Faces can be intuitively divided into regions such as the mouth, eyes, nose, etc., and these regions can be associated with the states of an HMM. The identi cation performance of a top-bottom HMM compares favourably with some of the well-known algorithms, for example eigenfaces as detailed in [8]. However, the HMM parameterisation in the work presented so far was arrived at by subjective intuition. This paper presents experimental results which show how identi cation rates vary with HMM parameters, and which indicate the most sensible choice of parameters. The paper is organised as follows: section 2 gives an overview of the HMM-based approach; section 3 details the training and recognition processes; section 4 describes the experimental setup; section 5 presents the identi cation results; section 6 concludes the paper.

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. 2011 Elsevier Ltd. All rights reserved.

This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.


Question: Face recognition through the merging of quantized skin color areas and wavelet packet analysis.

We have engineered a real-time computer system capable of identifying and tracking a subject's head, and subsequently recognizing the individual by analyzing facial features against a database of known profiles. This system's computational methodology is inspired by physiological studies, information theory, and the practical need for real-time performance and precision. Our strategy uniquely addresses the face recognition challenge as a fundamentally two-dimensional (2-D) problem, capitalizing on the fact that faces are typically oriented upright, allowing for their description through a limited set of characteristic 2-D views. The system operates by mapping face images onto a feature space that encompasses the significant variations observed in known facial images. This feature space is defined by "eigenfaces," which are the eigenvectors or principal components derived from a collection of faces, not necessarily corresponding to specific facial features like eyes, ears, or noses. Through projection onto this feature space, a face can be represented by a weighted sum of eigenface components, enabling the recognition of a particular face by comparing these weights with those of known individuals. Key benefits of our approach include the capacity to autonomously learn and recognize new faces, and its compatibility with neural network architectures for straightforward implementation.

We analyze the usage of Speeded Up Robust Features (SURF) as local descriptors for face recognition. The effect of different feature extraction and viewpoint consistency constrained matching approaches are analyzed. Furthermore, a RANSAC based outlier removal for system combination is proposed. The proposed approach allows to match faces under partial occlusions, and even if they are not perfectly aligned or illuminated. Current approaches are sensitive to registration errors and usually rely on a very good initial alignment and illumination of the faces to be recognized. A grid-based and dense extraction of local features in combination with a block-based matching accounting for different viewpoint constraints is proposed, as interest-point based feature extraction approaches for face recognition often fail. The proposed SURF descriptors are compared to SIFT descriptors. Experimental results on the AR-Face and CMU-PIE database using manually aligned faces, unaligned faces, and partially occluded faces show that the proposed approach is robust and can outperform current generic approaches.

ses a facial expression recognition system based on Gabor feature using a novel r bank. Traditionally, a global Gabor filter bank with 5 frequencies and 8 ten used to extract the Gabor feature. A lot of time will be involved to extract mensions of such Gabor feature vector are prohibitively high. A novel local Gabor art of frequency and orientation parameters is proposed. In order to evaluate the he local Gabor filter bank, we first employed a two-stage feature compression LDA to select and compress the Gabor feature, then adopted minimum distance nize facial expression. Experimental results show that the method is effective for eduction and good recognition performance in comparison with traditional entire . The best average recognition rate achieves 97.33% for JAFFE facial expression abor filter bank, feature extraction, PCA, LDA, facial expression recognition. deliver rich information about human emotion and play an essential role in human In order to facilitate a more intelligent and natural human machine interface of new cts, automatic facial expression recognition [1][18][20] had been studied world en years, which has become a very active research area in computer vision and n. There are many approaches have been proposed for facial expression analysis ages and image sequences [12][18] in the literature. we focus on the recognition of facial expression from single digital images with feature extraction. A number of approaches have been developed for extracting by: Motorola Labs Research Foundation (No.303D804372), NSFC (No.60275005), GDNSF 105938).

Now days it has become trend to follow all the celebrities as we consider them as our role models. So instead of searching the images of various celebrities in different websites we can find them in a single website by sorting all the images. Reliable database of images is essential for any image recognition system. Through Internet we find all the required images. These images will serve as samples for automatic recognition system. With these images we do face detection, face recognition, face sorting using various techniques like local binary patterns, haar cascades. We make an overall analysis of the detector. Using opencv we detect and recognize images. Similarity matching is done to check how the images are related to each other. Collection of images is based on user defined templates, which are in web browser environment. With the help of this system one can give their requirement and the image of celebrity is displayed based on that.

Face recognition systems play a vital role in many applications including surveillance, biometrics and security. In this work, we present a {\textit complete} real-time face recognition system consisting of a face detection, a recognition and a down sampling module using an FPGA. Our system provides an end-to-end solution for face recognition, it receives video input from a camera, detects the locations of the face(s) using the Viola-Jones algorithm, subsequently recognizes each face using the Eigenface algorithm, and outputs the results to a display. Experimental results show that our complete face recognition system operates at 45 frames per second on a Virtex-5 FPGA.

It is well recognized that expressions can significantly change facial geometry that results in a severe problem for robust 3D face recognition. So it is crucial for many applications that how to extract expression-robust features to describe 3D faces. In this paper, we develop a novel 3D face recognition algorithm using Local Binary Pattern (LBP) under expression varieties, which is an extension of the LBP operator widely used in ordinary facial analysis. First, to depict the human face more accurately and reduce the effect of facial local distortion for face recognition, a special feature-based 3D face division scheme is proposed. Then, the LBP representation framework for 3D faces is described, and the facial depth and normal information are extracted and encoded by LBP, to reduce the expression effect. For each face region, the statistical histogram is utilized to summarize the facial details, and accordingly three matching strategies are presented to address the recognition task. Finally, the proposed 3D face recognition algorithm is tested on BJUT-3D and FRGC v2.0 databases, achieves promising results, and concludes that it is feasible and valid to apply the LBP representation framework on 3D face recognition. & 2012 Elsevier B.V. All rights reserved.

Ear classification refers to the process by which an input ear image is assigned to one of several pre-defined classes based on a set of features extracted from the image. In the context of large-scale ear identification, where the input probe image has to be compared against a large set of gallery images in order to locate a matching identity, classification can be used to restrict the matching process to only those images in the gallery that belong to the same class as the probe. In this work, we utilize an unsupervised clustering scheme to partition ear images into multiple classes (i.e., clusters), with each class being denoted by a prototype or a centroid. A given ear image is assigned class labels (i.e., cluster indices) that correspond to the clusters whose centroids are closest to it. We compare the classification performance of three different texture descriptors, viz. Histograms of Oriented Gradients, uniform Local Binary Patterns and Local Phase Quantization. Extensive experiments using three different ear datasets suggest that the Local Phase Quantization texture descriptor scheme along with PCA for dimensionality reduction results in a 96.89% hit rate (i.e., 3.11% pre-selection error rate) with a penetration rate of 32.08%. Further, we demonstrate that the hit rate improves to 99.01% with a penetration rate of 47.10% when a multi-cluster search strategy is employed.

This paper presents the study on identification and classification of food grains using different color models such as L*a*b, HSV, HSI and YCbCr by combining color and texture features without performing preprocessing. The K-NN and minimum distance classifier are used to identify and classify the different types of food grains using local and global features. Texture and color features are the important features used in the classification of different objects. The local features like Haralick features are computed from co-occurrence matrix as texture features and global features from cumulative histogram are computed along with color features. The experiment was carried out on different food grains classes. The non-uniformity of RGB color space is eliminated by L*a*b, HSV, HSI and YCbCr color space. The correct classification result achieved for different color models is quite good. KeywordsFeature Extraction; co-occurrence matrix; texture information; Global Features; cumulative histogram; RGB, L*a*b, HSV, HSI and YCbCr color model.

Holistic face recognition algorithms are sensitive to expressions, illumination, pose, occlusions and makeup. On the other hand, feature-based algorithms are robust to such variations. In this paper, we present a feature-based algorithm for the recognition of textured 3D faces. A novel keypoint detection technique is proposed which can repeatably identify keypoints at locations where shape variation is high in 3D faces. Moreover, a unique 3D coordinate basis can be defined locally at each keypoint facilitating the extraction of highly descriptive pose invariant features. A 3D feature is extracted by fitting a surface to the neighborhood of a keypoint and sampling it on a uniform grid. Features from a probe and gallery face are projected to the PCA subspace and matched. The set of matching features are used to construct two graphs. The similarity between two faces is measured as the similarity between their graphs. In the 2D domain, we employed the SIFT features and performed fusion of the 2D and 3D features at the feature and score-level. The proposed algorithm achieved 96.1% identification rate and 98.6% verification rate on the complete FRGC v2 data set.

Facial composites are widely used by law enforcement agencies to assist in the identification and apprehension of suspects involved in criminal activities. These composites, generated from witness descriptions, are posted in public places and media with the hope that some viewers will provide tips about the identity of the suspect. This method of identifying suspects is slow, tedious, and may not lead to the timely apprehension of a suspect. Hence, there is a need for a method that can automatically and efficiently match facial composites to large police mugshot databases. Because of this requirement, facial composite recognition is an important topic for biometrics researchers. While substantial progress has been made in nonforensic facial composite (or viewed composite) recognition over the past decade, very little work has been done using operational composites relevant to law enforcement agencies. Furthermore, no facial composite to mugshot matching systems have been documented that are readily deployable as standalone software. Thus, the contributions of this paper include: 1) an exploration of composite recognition use cases involving multiple forms of facial composites; 2) the FaceSketchID System, a scalable, and operationally deployable software system that achieves state-of-the-art matching accuracy on facial composites using two algorithms (holistic and component based); and 3) a study of the effects of training data on algorithm performance. We present experimental results using a large mugshot gallery that is representative of a law enforcement agency's mugshot database. All results are compared against three state-of-the-art commercial-off-the-shelf face recognition systems.

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.


Question: Face recognition employing quantized skin tone area fusion and wavelet packet examination.

This paper elucidates the capacity of the multilayer perceptron (MLP), when employed as a classifier and trained via backpropagation, to serve as an approximation to the Bayes optimal discriminant function. This finding is substantiated for both binary and multi-class classification scenarios. It is demonstrated that the outputs generated by the MLP closely mimic the a posteriori probability functions associated with the classes under training. The proof of this assertion is comprehensive, encompassing MLPs with arbitrary numbers of layers and accommodating both linear and nonlinear unit activation functions.

**Question**: A Comparative Study of Different Evolutionary Algorithms for the Travelling Salesman Problem

**Documents**: A multiobjective evolutionary algorithm for the design of fuzzy controllers
This paper presents a new multiobjective evolutionary algorithm for the design of fuzzy controllers. The algorithm is based on the non-dominated sorting genetic algorithm II (NSGA-II) and is able to find a set of non-dominated solutions in a single run. The algorithm is applied to the design of a fuzzy controller for a nonlinear system. The results show that the algorithm is able to find a set of good quality solutions in a reasonable time.

**Similar Documents**: This paper introduces an innovative multiobjective evolutionary algorithm, tailored for the design of fuzzy controllers, which builds upon the foundations of the non-dominated sorting genetic algorithm II (NSGA-II). This algorithm is capable of discovering a diverse set of non-dominated solutions in a single execution, thereby providing a comprehensive exploration of the solution space. Its effectiveness is demonstrated through application to the design of a fuzzy controller for a nonlinear system. The outcomes reveal the algorithm's proficiency in identifying a collection of high-quality solutions within a feasible timeframe, showcasing its potential for optimizing fuzzy controllers in complex systems.

In this paper we describe a trainable object detector and its instantiations for detecting faces and cars at any size, location, and pose. To cope with variation in object orientation, the detector uses multiple classifiers, each spanning a different range of orientation. Each of these classifiers determines whether the object is present at a specified size within a fixed-size image window. To find the object at any location and size, these classifiers scan the image exhaustively. Each classifier is based on the statistics of localized parts. Each part is a transform from a subset of wavelet coefficients to a discrete set of values. Such parts are designed to capture various combinations of locality in space, frequency, and orientation. In building each classifier, we gathered the class-conditional statistics of these part values from representative samples of object and non-object images. We trained each classifier to minimize classification error on the training set by using Adaboost with Confidence-Weighted Predictions (Shapire and Singer, 1999). In detection, each classifier computes the part values within the image window and looks up their associated class-conditional probabilities. The classifier then makes a decision by applying a likelihood ratio test. For efficiency, the classifier evaluates this likelihood ratio in stages. At each stage, the classifier compares the partial likelihood ratio to a threshold and makes a decision about whether to cease evaluation—labeling the input as non-object—or to continue further evaluation. The detector orders these stages of evaluation from a low-resolution to a high-resolution search of the image. Our trainable object detector achieves reliable and efficient detection of human faces and passenger cars with out-of-plane rotation.

Recently Viola et al. [5] have introduced a rapid object detection scheme based on a boosted cascade of simple features. In this paper we introduce a novel set of rotated haar-like features, which significantly enrich this basic set of simple haar-like features and which can also be calculated very efficiently. At a given hit rate our sample face detector shows off on average a 10% lower false alarm rate by means of using these additional rotated features. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%. Using both enhancements the number of false detections is only 24 at a hit rate of 82.3% on the CMU face set [7].

At present there are many methods that could deal well with frontal view face recognition. However, most of them cannot work well when there is only one training image per person. In this paper, an extension of the eigenface technique, i.e. (PC)A, is proposed. (PC)A combines the original face image with its horizontal and vertical projections and then performs principal component analysis on the enriched version of the image. It requires less computational cost than the standard eigenface technique and experimental results show that on a gray-level frontal view face database where each person has only one training image, (PC)A achieves 3%-5% higher accuracy than the standard eigenface technique through using 10%-15% fewer eigenfaces.

In this paper, we present a novel face detection approach based on a convolutional neural architecture, designed to robustly detect highly variable face patterns, rotated up to /spl plusmn/20 degrees in image plane and turned up to /spl plusmn/60 degrees, in complex real world images. The proposed system automatically synthesizes simple problem-specific feature extractors from a training set of face and nonface patterns, without making any assumptions or using any hand-made design concerning the features to extract or the areas of the face pattern to analyze. The face detection procedure acts like a pipeline of simple convolution and subsampling modules that treat the raw input image as a whole. We therefore show that an efficient face detection system does not require any costly local preprocessing before classification of image areas. The proposed scheme provides very high detection rate with a particularly low level of false positives, demonstrated on difficult test sets, without requiring the use of multiple networks for handling difficult cases. We present extensive experimental results illustrating the efficiency of the proposed approach on difficult test sets and including an in-depth sensitivity analysis with respect to the degrees of variability of the face patterns.

Despite new technologies that make face detection and recognition more sophisticated, long-recognized problems in security, privacy, and accuracy persist. Refining this technology and introducing it into new domains will require solving these problems through focused interdisciplinary efforts among developers, researchers, and policymakers.

Face Recognition is a common problem in Machine Learning. This technology has already been widely used in our lives. For example, Facebook can automatically tag people’s faces in images, and also some mobile devices use face recognition to protect private security. Face images comes with different background, variant illumination, different facial expression and occlusion. There are a large number of approaches for the face recognition. Different approaches for face recognition have been experimented with specific databases which consist of single type, format and composition of image. Doing so, these approaches don’t suit with different face databases. One of the basic face recognition techniques is eigenface which is quite simple, efficient, and yields generally good results in controlled circumstances. So, this paper presents an experimental performance comparison of face recognition using Principal Component Analysis (PCA) and Normalized Principal Component Analysis (NPCA). The experiments are carried out on the ORL (ATT) and Indian face database (IFD) which contain variability in expression, pose, and facial details. The results obtained for the two methods have been compared by varying the number of training images. MATLAB is used for implementing algorithms also.

Use of Biometrics and widespread acceptability for person authentication has instigated several techniques. Now a day’s security is very important. Biometrics has received a great attention. Face biometrics, is useful person’s authentication that recognizes face. In this paper first we represent an overview of face recognition and discuss the methodology and functioning. Thereafter we represent face recognition techniques which are recently used including their advantages and disadvantages. A large number of face recognition algorithms have been developed. In this paper an attempt is made to review a wide range of methods including PCA, Eigen face, LDA for recognition and hybrid combination if this techniques. Under the various illumination and expression condition of face images some techniques specified here also improves the efficiency of face recognition. Keywords— Face Recognition, Principal Component Analysis, Eigen Face, Linear Discriminant Analysis

ses a facial expression recognition system based on Gabor feature using a novel r bank. Traditionally, a global Gabor filter bank with 5 frequencies and 8 ten used to extract the Gabor feature. A lot of time will be involved to extract mensions of such Gabor feature vector are prohibitively high. A novel local Gabor art of frequency and orientation parameters is proposed. In order to evaluate the he local Gabor filter bank, we first employed a two-stage feature compression LDA to select and compress the Gabor feature, then adopted minimum distance nize facial expression. Experimental results show that the method is effective for eduction and good recognition performance in comparison with traditional entire . The best average recognition rate achieves 97.33% for JAFFE facial expression abor filter bank, feature extraction, PCA, LDA, facial expression recognition. deliver rich information about human emotion and play an essential role in human In order to facilitate a more intelligent and natural human machine interface of new cts, automatic facial expression recognition [1][18][20] had been studied world en years, which has become a very active research area in computer vision and n. There are many approaches have been proposed for facial expression analysis ages and image sequences [12][18] in the literature. we focus on the recognition of facial expression from single digital images with feature extraction. A number of approaches have been developed for extracting by: Motorola Labs Research Foundation (No.303D804372), NSFC (No.60275005), GDNSF 105938).

This paper proposes a face recognition system, based on probabilistic decision-based neural networks (PDBNN). With technological advance on microelectronic and vision system, high performance automatic techniques on biometric recognition are now becoming economically feasible. Among all the biometric identification methods, face recognition has attracted much attention in recent years because it has potential to be most nonintrusive and user-friendly. The PDBNN face recognition system consists of three modules: First, a face detector finds the location of a human face in an image. Then an eye localizer determines the positions of both eyes in order to generate meaningful feature vectors. The facial region proposed contains eyebrows, eyes, and nose, but excluding mouth (eye-glasses will be allowed). Lastly, the third module is a face recognizer. The PDBNN can be effectively applied to all the three modules. It adopts a hierarchical network structures with nonlinear basis functions and a competitive credit-assignment scheme. The paper demonstrates a successful application of PDBNN to face recognition applications on two public (FERET and ORL) and one in-house (SCR) databases. Regarding the performance, experimental results on three different databases such as recognition accuracies as well as false rejection and false acceptance rates are elaborated. As to the processing speed, the whole recognition process (including PDBNN processing for eye localization, feature extraction, and classification) consumes approximately one second on Sparc10, without using hardware accelerator or co-processor.

We study the question of feature sets for robust visual object recognition; adopting linear SVM based human detection as a test case. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. We study the influence of each stage of the computation on performance, concluding that fine-scale gradients, fine orientation binning, relatively coarse spatial binning, and high-quality local contrast normalization in overlapping descriptor blocks are all important for good results. The new approach gives near-perfect separation on the original MIT pedestrian database, so we introduce a more challenging dataset containing over 1800 annotated human images with a large range of pose variations and backgrounds.


Question: Redirected walking technique for virtual reality environments.

This paper extends the findings of a previous study by Slater et al. [1995], which highlighted that participants in immersive virtual environments (VEs) exhibit a greater sense of presence when they move through virtual walking (walking-in-place) compared to using push-button flying. Our research expanded on this by incorporating real walking as an additional condition for comparison. Our results supported the initial findings, reinforcing that real walking surpasses both virtual walking and flying in terms of ease of use and naturalness as a locomotion method in VEs. The most pronounced disparity in subjective presence was observed between those who flew and the walkers—both real and virtual. Furthermore, we discovered that real walking provided a higher sense of presence than virtual walking, although this difference reached statistical significance only in certain scenarios. Subsequent investigations revealed that virtual walking can be significantly enhanced through the detection of footfalls using a head-mounted accelerometer. As observed in Slater's study, there was a strong correlation between subjective presence and the degree to which subjects felt embodied by their virtual avatars. This was our most robust statistical finding, indicating that substantial gains in presence could potentially be achieved by tracking all body parts and personalizing avatar appearance to match the user. An unintended but remarkable outcome was that real walking through an augmented version of Slater's visual cliff virtual environment led to an exceptionally immersive virtual experience, one that was profoundly compelling for us and the majority of our visitors. To fully capitalize on these findings, the most critical system enhancement would be the integration of wireless technology for all user connections. CR

This revised version maintains the length, complexity, and semantic style of the original document while preserving its relationship to the question. The document discusses a study that compares different locomotion methods in virtual environments and finds that real walking provides the highest sense of presence and ease of use. The study also suggests that virtual walking can be improved through the use of head-mounted accelerometers and that customizing avatar appearance could lead to further presence gains. The document concludes with a call for the integration of wireless technology to enhance the virtual experience.

Controlling a virtual vehicle is a sensory-motor activity with a specific rendering methodology that depends on the hardware technology and the software in use. We propose a method that computes haptic feedback for the steering wheel. It is best suited for low-cost, fixed-base driving simulators but can be ported to any driving simulator platform. The goal of our method is twofold. 1) It provides an efficient yet simple algorithm to model the steering mechanism using a quadri-polar representation. 2) This model is used to compute the haptic feedback on top of which a tunable haptic augmentation is adjusted to overcome the lack of presence and the unavoidable simulation loop latencies. This algorithm helps the driver to laterally control the virtual vehicle. We also discuss the experimental results that demonstrate the usefulness of our haptic feedback method.

We propose an intuitive user interaction system with virtual objects by directly using user's hands through a physics simulation. This system aims to provide a more direct way to manipulate and interact with virtual objects and to induce more realistic reactions of the virtual objects in the interaction than the existing methods. The key contribution of this paper is to model the deformations of the user's hands in two ways: global deformation by the hand poses and the local deformation by contacts with objects. By the two deformation models, objects can be manipulated with better stability and robustness without any predefinition of interaction methods depending on the object shapes, by solely using a physics simulation. The experiment shows that the proposed method enables a user to intuitively manipulate virtual object accurately.

Repetitive practices can facilitate the rehabilitation of the motor functions of the upper extremity for stroke survivors whose motor functions have been impaired. An affordable rehabilitation system for hand motion would be useful to provide intensive training to the patients. Augmented Reality (AR) can provide an intuitive interface to the users with programmable environment and realism feelings. In this research, a low cost viable rehabilitation system for hand motion is developed based on the AR technology. In this application, seamless interaction between the virtual environment and the real hand is supported with markers and a self-designed data-glove. The data-glove is low cost ((

This paper presents solutions for one requirement of autonomous characters in animation and games: the ability to navigate around their world in a life-like and improvisational manner. These “steering behaviors” are largely independent of the particulars of the character’s means of locomotion. Combinations of steering behaviors can be used to achieve higher level goals (For example: get from here to there while avoiding obstacles, follow this corridor, join that group of characters...) This paper divides motion behavior into three levels. It will focus on the middle level of steering behaviors, briefly describe the lower level of locomotion, and touch lightly on the higher level of goal setting and strategy.

Augmented reality technology is a unique medium that can be helpful for young children's entertainment and education, but in order to achieve the benefits of this technology, augmented reality experiences need to be appropriately designed for young children's developing physical and cognitive skills. In the present study we investigated how 5-10 year-old children react to typical handheld augmented reality interaction techniques such as crosshair selection and finger selection, in AR environments that require them to change perspective or not. Our analysis shows significant impacts of age upon AR performance, with young children having slower selection times, more tracking losses, and taking longer to recover tracking. Significant differences were also found between AR interaction technique conditions, with finger selection being faster than crosshair selection, and interactions which required changes in perspective taking longer, generating more tracking losses, and more errors in selection. Furthermore, by analyzing children's performance in relation to metrics of physical and cognitive development, we identified correlations between AR interaction techniques performance and developmental tests of spatial relations, block construction and visuomotor precision. Gender differences were analyzed but no significant effects were detected.

Meltran V, a new biped robot with telescopic legs, is introduced. For 3D walking control of the robot we analyze the dynamics of a three-dimensional inverted pendulum in which motion is constrained to move along an arbitrarily de ned plane. From this analysis we obtain simple linear dynamics, the ThreeDimensional Linear Inverted Pendulum Mode (3DLIPM). Using a real-time control method based on 3DLIPM, the Meltran V robot successfully demonstrated 3D dynamic walking without the use of any prepared trajectories.

The tourism sector has found virtual reality technology to be a good way to market travel destinations for consumers. In this paper, we describe two user studies with three web-based 3D virtual travel destination marketing portals. These three portals were developed to support and attract wintertime tourism into the region by offering a possibility to experience in advance, a virtual snowy scenery with different activities, for example downhill skiing. In both user studies with 21 subjects the focus was on user experience with the 3D virtual travel destination marketing portals. In the second study also the visual design aspects within these portals were studied. Our studies indicate that 3D virtual travel destination marketing portals can enhance 2D web pages, if they offer the possibility to explore the location freely and through different kinds of virtual activities. Also our studies support prior findings of the efficiency of glow effect for indicating interactive 3D elements within a 3D virtual environment.

We present design and impact studies of visual feedback for virtual grasping. The studies suggest new or updated guidelines for feedback. Recent grasping techniques incorporate visual cues to help resolve undesirable visual or performance artifacts encountered after real fingers enter a virtual object. Prior guidelines about such visuals are based largely on other interaction types and provide inconsistent and potentially-misleading information when applied to grasping. We address this with a two-stage study. In the first stage, users adjusted parameters of various feedback types, including some novel aspects, to identify promising settings and to give insight into preferences regarding the parameters. In the next stage, the tuned feedback techniques were evaluated in terms of objective performance (finger penetration, release time, and precision) and subjective rankings (visual quality, perceived behavior impact, and overall preference). Additionally, subjects commented on the techniques while reviewing them in a final session. Performance wise, the most promising techniques directly reveal penetrating hand configuration in some way. Subjectively, subjects appreciated visual cues about interpenetration or grasp force, and color changes are most promising. The results enable selection of the best cues based on understanding the relevant tradeoffs and reasonable parameter values. The results also provide a needed basis for more focused studies of specific visual cues and for choosing conditions in comparisons to other feedback modes, such as haptic, audio, or multimodal. Considering results, we propose that 3D interaction guidelines must be updated to capture the importance of interpenetration cues, possible performance benefits of direct representations, and tradeoffs involved in cue selection.

Wearable computing moves computation from the desktop to the user. We are forming a community of networked, wearable-computer users to explore, over a long period, the augmented realities that these systems can provide. By adapting its behavior to the user's changing environment, a body-worn computer can assist the user more intelligently, consistently, and continuously than a desktop system. A text-based augmented reality, the Remembrance Agent, is presented to illustrate this approach. Video cameras are used both to warp the visual input (mediated reality) and to sense the user's world for graphical overlay. With a camera, the computer could track the user's finger to act as the system's mouse; perform face recognition; and detect passive objects to overlay 2.5D and 3D graphics onto the real world. Additional apparatus such as audio systems, infrared beacons for sensing location, and biosensors for learning about the wearer's affect are described. With the use of input from these interface devices and sensors, a long-term goal of this project is to model the user's actions, anticipate his or her needs, and perform a seamless interaction between the virtual and physical environments.

In Augmented Reality, see-through HMDs superimpose virtual 3D objects on the real world. This technology has the potential to enhance a user's perception and interaction with the real world. However, many Augmented Reality applications will not be accepted until we can accurately register virtual objects with their real counterparts. In previous systems, such registration was achieved only from a limited range of viewpoints, when the user kept his head still. This paper offers improved registration in two areas. First, our system demonstrates accurate static registration across a wide variety of viewing angles and positions. An optoelectronic tracker provides the required range and accuracy. Three calibration steps determine the viewing parameters. Second, dynamic errors that occur when the user moves his head are reduced by predicting future head locations. Inertial sensors mounted on the HMD aid head-motion prediction. Accurate determination of prediction distances requires low-overhead operating systems and eliminating unpredictable sources of latency. On average, prediction with inertial sensors produces errors 2-3 times lower than prediction without inertial sensors and 5-10 times lower than using no prediction at all. Future steps that may further improve registration are outlined.


Question: Virtual reality navigation through redirected walking.

This paper explores the "Point & Teleport" locomotion technique for virtual reality (VR) environments, offering a novel solution to the challenge of moving a user's viewpoint within virtual spaces. As VR technology becomes more accessible and popular, with new devices hitting the market at lower price points, there has been a surge in the development of VR video games. These games often adopt first-person interaction methods, which are particularly well-suited for head-mounted displays (HMDs). Among the various interaction techniques, locomotion plays a crucial role in enhancing user experience, as it allows users to navigate virtual environments. The "Point & Teleport" technique stands out by enabling users to instantly transport themselves to any desired location within the virtual world simply by pointing. This method is advantageous in that it minimizes the risk of motion sickness, as it does not involve any visible movement. The paper presents two experiments designed to evaluate the "Point & Teleport" technique against two established VR locomotion methods: walk-in-place and joystick control. In the first experiment, participants were asked to compare the "Point & Teleport" technique with the traditional walk-in-place and joystick methods. In the second experiment, an additional feature was introduced to the "Point & Teleport" technique, allowing users to specify not only their destination but also their orientation upon arrival. Sixteen participants took part in both experiments. The results indicated that the "Point & Teleport" technique was highly regarded for its ease of use and enjoyable experience, although the addition of the orientation component led to a slight decrease in user satisfaction.

Age-related declines in spatial navigation are well-known in human and non-human species. Studies in non-human species suggest that alteration in hippocampal and other neural circuitry may underlie behavioral deficits associated with aging but little is known about the neural mechanisms of human age-related decline in spatial navigation. The purpose of the present study was to examine age differences in functional brain activation during virtual environment navigation. Voxel-based analysis of activation patterns in young subjects identified activation in the hippocampus and parahippocampal gyrus, retrosplenial cortex, right and left lateral parietal cortex, medial parietal lobe and cerebellum. In comparison to younger subjects, elderly participants showed reduced activation in the hippocampus and parahippocampal gyrus, medial parietal lobe and retrosplenial cortex. Relative to younger participants elderly subjects showed increased activation in anterior cingulate gyrus and medial frontal lobe. These results provide evidence of age specific neural networks supporting spatial navigation and identify a putative neural substrate for age-related differences in spatial memory and navigational skill.

The cortical, cerebellar and brainstem BOLD-signal changes have been identified with fMRI in humans during mental imagery of walking. In this study the whole brain activation and deactivation pattern during real locomotion was investigated by [(18)F]-FDG-PET and compared to BOLD-signal changes during imagined locomotion in the same subjects using fMRI. Sixteen healthy subjects were scanned at locomotion and rest with [(18)F]-FDG-PET. In the locomotion paradigm subjects walked at constant velocity for 10 min. Then [(18)F]-FDG was injected intravenously while subjects continued walking for another 10 min. For comparison fMRI was performed in the same subjects during imagined walking. During real and imagined locomotion a basic locomotion network including activations in the frontal cortex, cerebellum, pontomesencephalic tegmentum, parahippocampal, fusiform and occipital gyri, and deactivations in the multisensory vestibular cortices (esp. superior temporal gyrus, inferior parietal lobule) was shown. As a difference, the primary motor and somatosensory cortices were activated during real locomotion as distinct to the supplementary motor cortex and basal ganglia during imagined locomotion. Activations of the brainstem locomotor centers were more prominent in imagined locomotion. In conclusion, basic activation and deactivation patterns of real locomotion correspond to that of imagined locomotion. The differences may be due to distinct patterns of locomotion tested. Contrary to constant velocity real locomotion (10 min) in [(18)F]-FDG-PET, mental imagery of locomotion over repeated 20-s periods includes gait initiation and velocity changes. Real steady-state locomotion seems to use a direct pathway via the primary motor cortex, whereas imagined modulatory locomotion an indirect pathway via a supplementary motor cortex and basal ganglia loop.

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.

Robot navigation in the environment with obstacles is still a challenging problem. In this paper, the navigation problems with wheeled mobile robots (WMRs) are reviewed, the navigation mechanism of WMRs is analyzed in detail, the methods of solving the sub problems such as mapping, localization and path planning which all both related to robot navigation are summarized and the advantages and disadvantages of the existing methods are expounded. Especially in the agricultural field, the precise navigation of robots in the complex agricultural environment is the prerequisite for the completion of various tasks. This paper is aimed at the special complexity of the agricultural environment, prospected the application of the solution to the navigation problem of WMRs in agricultural engineering, put forward the research direction to solve the problems of precise navigation in agricultural environments.

WAVES, a Wearable Asymmetric Vibration Excitation System, is a novel wearable haptic device for presenting three dimensions of translation and rotation guidance cues. In contrast to traditional vibration feedback, which usually requires that users learn to interpret a binary cue, asymmetric vibrations have been shown to induce a pulling sensation in a desired direction. When attached to the fingers, a single voicecoil actuator presents a translation guidance cue and a pair of voicecoil actuators presents a rotation guidance cue. The directionality of mechanoreceptors in the skin led to our choice of the location and orientation of the actuators in order to elicit very strong sensations in certain directions. For example, users distinguished a "left" cue versus a "right" cue 94.5% of the time. When presented with one of six possible direction cues, users on average correctly identified the direction of translation cues 86.1% of the time and rotation cues 69.0% of the time.

In this paper we present a markerless augmented reality (MAR) approach based on real-time 3D reconstruction using a low-cost depth camera, the Kinect. We intend to use this MAR approach for 3D medical visualization. In the actual state of developing, our approach is based on the KinectFusion algorithm with some minor adaptations, as long as our goal is not reconstruct scenes but use the reconstructed model as a reference to MAR. Keywords-Augmented Reality; 3D Reconstruction; Kinect;

We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.

We present MRTouch, a novel multitouch input solution for head-mounted mixed reality systems. Our system enables users to reach out and directly manipulate virtual interfaces affixed to surfaces in their environment, as though they were touchscreens. Touch input offers precise, tactile and comfortable user input, and naturally complements existing popular modalities, such as voice and hand gesture. Our research prototype combines both depth and infrared camera streams together with real-time detection and tracking of surface planes to enable robust finger-tracking even when both the hand and head are in motion. Our technique is implemented on a commercial Microsoft HoloLens without requiring any additional hardware nor any user or environmental calibration. Through our performance evaluation, we demonstrate high input accuracy with an average positional error of 5.4 mm and 95% button size of 16 mm, across 17 participants, 2 surface orientations and 4 surface materials. Finally, we demonstrate the potential of our technique to enable on-world touch interactions through 5 example applications.

This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.


Question: A method for redirected walking in virtual reality environments.

The "Visual Abyss": Investigating Depth Perception Across Species
In a groundbreaking experiment, researchers have utilized a simple yet effective apparatus, known as the visual cliff, to explore depth perception in various animal species. This apparatus challenges the subjects' ability to perceive and react to a simulated drop-off, revealing insights into their innate and learned spatial awareness.

Across species, the ability to perceive depth and avoid precipitous drops appears to be present at an early developmental stage, often before the animal has had any direct experience with heights or falls. This is particularly intriguing when considering human infants, who, despite their vulnerability to falls during the crawling and early walking stages, demonstrate a natural aversion to perceived drop-offs. Such behavior is typically safeguarded by the use of protective measures such as crib side panels, stair gates, and adult supervision. As infants grow and their motor skills improve, they naturally develop the ability to navigate their environment more safely, suggesting that depth perception is indeed an innate trait.

The question remains: is depth perception an inherent ability or a learned skill? To shed light on this, the visual cliff experiment offers a controlled environment to test the responses of different species to visual cues of depth. The setup involves a transparent surface (usually glass) placed over a drop-off, with one side having a solid appearance due to a patterned surface, while the other side creates the illusion of an abyss. This allows researchers to control visual stimuli while ensuring the safety of the test subjects.

The classic visual cliff experiment has revealed that animals such as rats, pups, kittens, and chicks instinctively avoid crossing over to the side that appears unsupported, even when enticed by the presence of their mothers or other stimuli. This behavior is consistent across species, indicating that depth perception is a fundamental, innate ability rather than a skill acquired through experience.

Such findings not only contribute to our understanding of depth perception in animals but also have implications for the broader study of spatial awareness and its development in humans and other species. The visual cliff experiment continues to serve as a valuable tool in the investigation of these complex perceptual processes.

We explore the development of an experimental augmented reality application that provides benefits to professional mechanics performing maintenance and repair tasks in a field setting. We developed a prototype that supports military mechanics conducting routine maintenance tasks inside an armored vehicle turret, and evaluated it with a user study. Our prototype uses a tracked headworn display to augment a mechanic's natural view with text, labels, arrows, and animated sequences designed to facilitate task comprehension, localization, and execution. A within-subject controlled user study examined professional military mechanics using our system to complete 18 common tasks under field conditions. These tasks included installing and removing fasteners and indicator lights, and connecting cables, all within the cramped interior of an armored personnel carrier turret. An augmented reality condition was tested against two baseline conditions: the same headworn display providing untracked text and graphics and a fixed flat panel display representing an improved version of the laptop-based documentation currently employed in practice. The augmented reality condition allowed mechanics to locate tasks more quickly than when using either baseline, and in some instances, resulted in less overall head movement. A qualitative survey showed that mechanics found the augmented reality condition intuitive and satisfying for the tested sequence of tasks.

Although Augmented Reality technology was first developed over forty years ago, there has been little survey work giving an overview of recent research in the field. This paper reviews the ten-year development of the work presented at the ISMAR conference and its predecessors with a particular focus on tracking, interaction and display research. It provides a roadmap for future augmented reality research which will be of great value to this relatively young field, and also for helping researchers decide which topics should be explored when they are beginning their own studies in the area.

In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.

Controlling a virtual vehicle is a sensory-motor activity with a specific rendering methodology that depends on the hardware technology and the software in use. We propose a method that computes haptic feedback for the steering wheel. It is best suited for low-cost, fixed-base driving simulators but can be ported to any driving simulator platform. The goal of our method is twofold. 1) It provides an efficient yet simple algorithm to model the steering mechanism using a quadri-polar representation. 2) This model is used to compute the haptic feedback on top of which a tunable haptic augmentation is adjusted to overcome the lack of presence and the unavoidable simulation loop latencies. This algorithm helps the driver to laterally control the virtual vehicle. We also discuss the experimental results that demonstrate the usefulness of our haptic feedback method.

This paper presents a robust and flexible framework for augmented reality which does not require instrumenting either the environment or the workpiece. A model-based visual tracking system is combined with with rate gyroscopes to produce a system which can track the rapid camera rotations generated by a head-mounted camera, even if images are substantially degraded by motion blur. This tracking yields estimates of head position at video field rate (50Hz) which are used to align computer-generated graphics on an optical see-through display. Nonlinear optimisation is used for the calibration of display parameters which include a model of optical distortion. Rendered visuals are pre-distorted to correct the optical distortion of the display.

Dante II is a unique walking robot that provides important insight into high-mobility robotic locomotion and remote robotic exploration. Dante II’s uniqueness stems from its combined legged and rappelling mobility system, its scanning-laser rangefinder, and its multilevel control scheme. In 1994 Dante II was deployed and successfully tested in a remote Alaskan volcano, as a demonstration of the fieldworthiness of these technologies. For more than five days the robot explored alone in the volcano crater using a combination of supervised autonomous control and teleoperated control. Human operators were located 120 km distant during the mission. This article first describes in detail the robot, support systems, control techniques, and user interfaces. We then describe results from the battery of field tests leading up to and including the volcanic mission. Finally, we put forth important lessons which comprise the legacy of this project. We show that framewalkers are appropriate for rappelling in severe terrain, though tether systems have limitations. We also discuss the importance of future “autonomous” systems to realize when they require human support rather than relying on humans for constant oversight. KEY WORDS—rappelling, walking robot, supervised autonomy, behavior-based control, terrain visualization, volcano exploration

A large Delta robot has been modified to pick up and place a triangular shaped foot. The robot pushes the foot down to lift the body and the three associated fixed support legs. It then moves the foot sideways so that the body moves in the opposite direction to a new position where the fixed legs are lowered to the ground and the foot lifted ready for a new placement. In this way the robot is able to move in any direction with a tripod gait. The use of a combined foot and gripper for moving heavy objects is investigated. In the implementation reported, the gearboxes used are found to limit the operation and spring assistance is investigated as a trade off with payload. The small prototype, stands as tall as a person, weighs a third of a ton, and has a payload capability of near 100%. It is envisaged as a prototype for a much larger walking robot capable of general-purpose heavy lifting.

In this paper we present a novel wearable navigation system along with implicit HCI (iHCI) model, where interaction with technology is dissolved into a day-today activity. In this type of HCI model a computer takes the input and tries to output an action that is a proactive anticipation of next action of a user. Usually, in urban areas people use voice assisted navigation systems or navigation guidelines displayed on a mobile phone. Some navigation systems are already installed on car dashboard, which needs explicit attention in order to make driving decisions. A navigation system using haptic perception to guide a user throughout a journey is the key contribution of this paper. It does not ask for explicit user attention and demonstrates the indolent form of technological interaction. This wearable device is an index finger sleeve, which consists of vibrator modules, Bluetooth communication module and Microcontroller Unit (MCU). A working prototype has been built and tested.

In this paper we present a low-cost and open-source brain-computer interface (BCI) virtual-reality (VR) Game Control Development Environment prototype, which we demonstrate using real-time signal processing of Electroencephalography (EEG) event-related desynchronization and synchronization changes (ERD/ERS) within the Precentral Gyrus (Motor Cortex), allowing a user to control a 3D object within a Virtual Reality Environment. This BCI-VR system prototype was functionally tested on multiple participants and demonstrated live before an audience during the 2017 ‘Hack the Brain’ at the Dublin Science Gallery. The availability of such an open-source, effective, BCI-VR Game Control Development Environment, at a level acceptable for industry experimentation, has the potential to open up this field to a much wider range of researchers and games developers and to assist the investigation of gaming experiences which both incorporate the specific control features available through BCI as a core element of the game play and the potential for its use in neurorehabilitation.

The cave automatic virtual environment (CAVE) system is one of the most fully immersive systems for virtual reality environments. By providing users with realistic perception and immersive experience, CAVE systems have been widely used in many fields, including military, education, health care, entertainment, design, and others. In this paper, we focus on the design applications in the CAVE. The design applications involve many interactions between the user and the CAVE. However, the conventional interaction tool, the wand, cannot provide fast and convenient interactions. In this paper, we propose vDesign, a CAVE-based virtual design environment using hand interactions. The hand interactions in vDesign are classified into menu navigation and object manipulations. For menu navigation, we define two interactions: activating the main menu and selecting a menu item. For object manipulations, we define three interactions: moving, rotating, and scaling an object. By using the proposed hand interactions, we develop the functions of image segmentation and image composition in vDesign. With the image segmentation function, the designer can select and cut the interested objects from different images. With the image composition function, the designer can manipulate the segmented objects and combine them as a composite image. We implemented the vDesign prototype in CAVE and conducted experiments to evaluate the interaction performance in terms of manipulation time and distortion. The experimental results demonstrated that the proposed hand interactions can provide faster and more accurate interactions compared to the traditional wand interactions.


Question: A method for redirected walking in virtual reality environments.

This study introduces Enhanced Redirection with Visual Distractions (ERV), an innovative locomotion interface designed to enhance navigation in virtual environments (VEs), particularly addressing the challenge of spatial limitations. ERV allows users to explore VEs that exceed the physical tracking boundaries without the need for predefined waypoints. To assess its effectiveness, we compared ERV against the benchmark interface, genuine walking, through a comprehensive user study focusing on navigational proficiency. The findings revealed that participants utilizing ERV could navigate through expansive VEs, equivalent to real-world walking, while maintaining comparable accuracy in pointing to targets and creating mental maps of the VE layout. This underscores the capability of ERV to facilitate realistic walking experiences within the constraints of limited physical spaces, thereby significantly enhancing the user's virtual navigation capabilities.

We present an overall framework of services for indoor navigation, which includes Indoor Mapping, Indoor Positioning, Path Planning, and En-route Assistance. Within such framework we focus on an augmented reality (AR) solution for en-route assistance. AR assists the user walking in a multi-floor building by displaying a directional arrow under a camera view, thus freeing the user from knowing his/her position. Our AR solution relies on geomagnetic positioning and north-oriented space coordinates transformation. Therefore, it can work without infrastructure and without relying on GPS. The AR visual interface and the integration with magnetic positioning is the main novelty of our solution, which has been validated by experiments and shows a good performance.

We propose a new user interface system for mobile devices. By using augmented reality (AR) technology, the system overlays virtual objects on real images captured by a camera attached to the back of a mobile device, and the user can operate the mobile device by manipulating the virtual objects with his/her hand in the space behind the mobile device. This system allows the user to operate the device in a wide three-dimensional space and to select small objects easily. Also, the AR technology provides the user with a sense of reality in operating the device. We developed a typing application using our system and verified the effectiveness by user studies. The results showed that more than half of the subjects felt that the operation area of the proposed system is larger than that of a smartphone and that both AR and unfixed key-plane are effective for improving typing speed.

The promise of being able to be inside another world might be resolved by Virtual Reality. It wouldn’t be naïve to assume that we will be able to enter and completely feel another world at some point in the future; be able to interact with knowledge and entertainment in a totally immersive state. Advancements are becoming more frequent, and with the recent popularity of technology in this generation, a lot of investment is being made. Prototypes of head displays that completely cover the user’s view and movement recognition which doesn’t need an intermediate device for input of data are already Virtual Reality devices available to developers and even to the public. From time to time, the way we interact with computers change, and virtual reality promises to make this interaction as real as possible. Although scenes like flying a jet or tank, are already tangible, another scenes, such as being able to feel the dry air of the Sahara in geography classes or feel the hard, cold scales of a dragon in a computer game seem to be in a long w a y f r o m n o w. H o w e v e r , t e c h n o l o g i c advancements and increase in the popularity of these technologies point to the possibility of such amazing scenes coming true.

The mission of tracking a pedestrian is valuable for many applications including walking distance estimation for the purpose of pervasive healthcare, museum and shopping mall guides, and locating emergency responders. In this paper, we show how accurate and ubiquitous tracking of a pedestrian can be performed using only the inertial sensors embedded in his/her mobile phone. Our work depends on performing dead reckoning to track the user's movement. The main challenge that needs to be addressed is handling the noise of the low cost low quality inertial sensors in cell phones. Our proposed system combines two novel contributions: a novel step count estimation technique and a gait-based accurate variable step size detection algorithm. The step count estimation technique is based on a lightweight finite state machine approach that leverages orientation-independent features. In order to capture the varying stride length of the user, based on his changing gait, we employ a multi-class hierarchical Support Vector Machine classifier. Combining the estimated number of steps with the an accurate estimate of the individual stride length, we achieve ubiquitous and accurate tracking of a person in indoor environments. We implement our system on different Android-based phones and compare it to the state-of-the-art techniques in indoor and outdoor testbeds with arbitrary phone orientation. Our results in two different testbeds show that we can provide an accurate step count estimation with an error of 5.72%. In addition, our gait type classifier has an accuracy of 97.74%. This leads to a combined tracking error of 6.9% while depending only on the inertial sensors and turning off the GPS sensor completely. This highlights the ability of the system to provide ubiquitous, accurate, and energy efficient tracking.

A two-handed direct manipulation VE (virtual environment) interface has been developed as an intuitive manipulation metaphor for graphical objects. A new input device called ChordGloves introduces a simple technique for rapid and repeatable gesture recognition; the Chordgloves emulate a pair of 3-D mice and a keyboard. A drafting table is isomorphically mapped into the VE and provides hand support for 2-D interface techniques, as well as a reference frame for calibrating the mapping between real and virtual worlds. A cursor gravity function is used to grab vertices, edges, or faces and establish precisely aligned differential constraints between objects called anchors. The capability of subjects to translate, rotate, scale, align, and glue objects is tested with a puzzle building task. An approximation of the puzzle task is done in Adobe Illustrator to provide a performance reference. Results and informal user observations as well as topics for future work are presented.

We present a real-time stereoscopic video-see-through augmented reality (AR) system applied to the medical procedure known as ultrasound-guided needle biopsy of the breast. The AR system was used by a physician during procedures on breast models and during non-invasive examinations of human subjects. The system merges rendered live ultrasound data and geometric elements with stereo images of the patient acquired through head-mounted video cameras and presents these merged images to the physician in a head-mounted display. The physician sees a volume visualization of the ultrasound data directly under the ultrasound probe, properly registered within the patient and with the biopsy needle. Using this system, a physician successfully guided a needle into an artificial tumor within a training phantom of a human breast. We discuss the construction of the AR system and the issues and decisions which led to the system architecture and the design of the video see-through head-mounted display. We designed methods to properly resolve occlusion of the real and synthetic image elements. We developed techniques for realtime volume visualization of timeand position-varying ultrasound data. We devised a hybrid tracking system which achieves improved registration of synthetic and real imagery and we improved on previous techniques for calibration of a magnetic tracker. CR

In the presented exploratory study, quantitative relations between the experience of architectural spaces and physical properties were investigated using virtual reality (VR) simulations. Geometric properties from a component-based description of rectangular rooms were tested on their suitability as predictor variables for experiential qualities. In a virtual-reality-based perceptual experiment, qualities of 16 vacant rectangular interiors were rated in eight principal categories by 16 participants using the semantic differential scaling technique. The simulated scenes were automatically generated by means of a custom made utility that also provided for the component-based room descriptions. The data analysis revealed strong correlations between several scene features and the averaged rated experience. For example, a preference for ratios near to the golden section could be observed for spatial proportions, which are not directly perceivable. Altogether, a set of five widely independent factors (openness, two room proportions, room area, and balustrade height) turned out to be most effective for describing the observed variance in the attributed experiential qualities. The combination of realistic virtual reality simulations and psychophysical data raising methods proved to be an effective means for basic architectural research. Several quantitative relations between physical properties and the emotional experience of architectural space could be demonstrated. D 2004 Elsevier B.V. All rights reserved.

We are interested in how the concept of affordances can affect our view to autonomous robot control, and how the results obtained from autonomous robotics can be reflected back upon the discussion and studies on the concept of affordances. In this paper, we studied how a mobile robot, equipped with a 3D laser scanner, can learn to perceive the traversability affordance and use it to wander in a room tilled with spheres, cylinders and boxes. The results showed that after learning, the robot can wander around avoiding contact with non-traversable objects (i.e. boxes, upright cylinders, or lying cylinders in certain orientation), but moving over traversable objects (such as spheres, and lying cylinders in a rollable orientation with respect to the robot) rolling them out of its way. We have shown that for each action approximately 1% of the perceptual features were relevant to determine whether it is afforded or not and that these relevant features are positioned in certain regions of the range image. The experiments are conducted both using a physics-based simulator and on a real robot.

Stereoscopic head-mounted displays (HMD's) provide an effective capability to create dynamic virtual environments. For a user of such environments, virtual objects would be displayed ideally at the appropriate distances, and natural concordant accommodation and convergence would be provided. Under such image display conditions, the user perceives these objects as if they were objects in a real environment. Current HMD technology requires convergent eye movements. However, it is currently limited by fixed visual accommodation, which is inconsistent with real-world vision. A prototype multiplanar volumetric projection display based on a stack of laminated planes was built for medical visualization as discussed in a paper presented at a 1999 Advanced Research Projects Agency workshop (Sullivan, Advanced Research Projects Agency, Arlington, Va., 1999). We show how such technology can be engineered to create a set of virtual planes appropriately configured in visual space to suppress conflicts of convergence and accommodation in HMD's. Although some scanning mechanism could be employed to create a set of desirable planes from a two-dimensional conventional display, multiplanar technology accomplishes such function with no moving parts. Based on optical principles and human vision, we present a comprehensive investigation of the engineering specification of multiplanar technology for integration in HMD's. Using selected human visual acuity and stereoacuity criteria, we show that the display requires at most 27 equally spaced planes, which is within the capability of current research and development display devices, located within a maximal 26-mm-wide stack. We further show that the necessary in-plane resolution is of the order of 5 microm.

Virtual reality systems are widely believed to be the next major computing platform. There are, however, some barriers to adoption that must be addressed, such as that of motion sickness — which can lead to undesirable symptoms including postural instability, headaches, and nausea. Motion sickness in virtual reality occurs as a result of moving visual stimuli that cause users to perceive self-motion while they remain stationary in the real world. There are several contributing factors to both this perception of motion and the subsequent onset of sickness, including field of view, motion velocity, and stimulus depth. We verify first that differences in vection due to relative stimulus depth remain correlated with sickness. Then, we build a dataset of stereoscopic 3D videos and their corresponding sickness ratings in order to quantify their nauseogenicity, which we make available for future use. Using this dataset, we train a machine learning algorithm on hand-crafted features (quantifying speed, direction, and depth as functions of time) from each video, learning the contributions of these various features to the sickness ratings. Our predictor generally outperforms a naïve estimate, but is ultimately limited by the size of the dataset. However, our result is promising and opens the door to future work with more extensive datasets. This and further advances in this space have the potential to alleviate developer and end user concerns about motion sickness in the increasingly commonplace virtual world.