Question: Detecting facial features: An approach based on facial symmetry.

This paper introduces a novel technique, Local Decorrelation, aimed at enhancing pedestrian detection accuracy while maintaining computational efficiency. Despite the emergence of complex, data-intensive methodologies, boosted decision trees continue to excel in rapid rigid object detection, consistently ranking among the most accurate approaches across various datasets. Typically, these boosted detectors employ decision trees with orthogonal (single feature) splits, which may not optimally align with the inherent topology of the data, particularly in cases of high feature correlation. To address this limitation, oblique (multiple feature) splits have been proposed; however, they significantly increase computational overhead. Drawing inspiration from recent advancements in discriminative decorrelation of Histograms of Oriented Gradients (HOG) features, we propose an efficient feature transformation that eliminates local correlations. This results in an overcomplete yet locally decorrelated representation that is perfectly suited for use with orthogonal decision trees. Remarkably, our orthogonal trees, when equipped with the locally decorrelated features, outperform oblique trees trained on the original features, achieving this superior performance at a fraction of the computational cost. The enhancement in accuracy is substantial: on the Caltech Pedestrian Dataset, our method decreases false positives by nearly an order of magnitude compared to the previous state-of-the-art.

**Question**: Investigating the impact of social media on academic performance among college students

**Documents**: The Effect of Facebook on Academic Performance in College Students
This study aimed to explore the relationship between Facebook usage and academic performance among college students. A survey was conducted among 300 undergraduate students from a large public university in the United States. The results revealed that excessive Facebook usage negatively correlated with academic performance, as measured by GPA. Moreover, the study found that the type of Facebook activity also played a role; passive consumption of content was more detrimental than active engagement. The findings suggest that educational institutions should consider educating students about the potential negative effects of excessive social media usage on academic performance.

**Similar Documents**: This research delved into the connection between Facebook usage and academic achievement among college undergraduates. A questionnaire was administered to 300 students enrolled in a prominent public university in the U.S. to gather data. The analysis showed a significant inverse relationship between heavy Facebook use and academic performance, as quantified by grade point average (GPA). Interestingly, the study highlighted that the nature of Facebook activity influenced the outcome; passive browsing was found to be more harmful than active participation. These results underscore the importance of institutions of higher learning in raising awareness among students about the adverse impacts of excessive social media consumption on

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.

In this paper, we present a deep regression approach for face alignment. The deep regressor is a neural network that consists of a global layer and multistage local layers. The global layer estimates the initial face shape from the whole image, while the following local layers iteratively update the shape with local image observations. Combining standard derivations and numerical approximations, we make all layers able to backpropagate error differentials, so that we can apply the standard backpropagation to jointly learn the parameters from all layers. We show that the resulting deep regressor gradually and evenly approaches the true facial landmarks stage by stage, avoiding the tendency that often occurs in the cascaded regression methods and deteriorates the overall performance: yielding early stage regressors with high alignment accuracy gains but later stage regressors with low alignment accuracy gains. Experimental results on standard benchmarks demonstrate that our approach brings significant improvements over previous cascaded regression algorithms.

Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.

We propose a novel end-to-end deep architecture for face landmark detection, based on a deep convolutional and deconvolutional network followed by carefully designed recurrent network structures. The pipeline of this architecture consists of three parts. Through the first part, we encode an input face image to resolution-preserved deconvolutional feature maps via a deep network with stacked convolutional and deconvolutional layers. Then, in the second part, we estimate the initial coordinates of the facial key points by an additional convolutional layer on top of these deconvolutional feature maps. In the last part, by using the deconvolutional feature maps and the initial facial key points as input, we refine the coordinates of the facial key points by a recurrent network that consists of multiple long short-term memory components. Extensive evaluations on several benchmark data sets show that the proposed deep architecture has superior performance against the state-of-the-art methods.

According to art theory, pictorial balance acts to unify picture elements into a cohesive composition. For asymmetrical compositions, balancing elements is thought to be similar to balancing mechanical weights in a framework of symmetry axes. Assessment of preference for balance (APB), based on the symmetry-axes framework suggested in Arnheim R, 1974 Art and Visual Perception: A Psychology of the Creative Eye (Berkeley, CA: University of California Press), successfully matched subject balance ratings of images of geometrical shapes over unlimited viewing time. We now examine pictorial balance perception of Japanese calligraphy during first fixation, isolated from later cognitive processes, comparing APB measures with results from balance-rating and comparison tasks. Results show high between-task correlation, but low correlation with APB. We repeated the rating task, expanding the image set to include five rotations of each image, comparing balance perception of artist and novice participant groups. Rotation has no effect on APB balance computation but dramatically affects balance rating, especially for art experts. We analyze the variety of rotation effects and suggest that, rather than depending on element size and position relative to symmetry axes, first fixation balance processing derives from global processes such as grouping of lines and shapes, object recognition, preference for horizontal and vertical elements, closure, and completion, enhanced by vertical symmetry.

In this research, a design and implementation of real-time face recognition engine using compact features vector extracted by linear binary pattern (LBP) and zoned discrete cosine transforms (DCT) analysis is proposed for electronics key. The function of LBP is to normalize the lighting variations of the input face image and the function of zoned DCT is to define the local descriptors of the face image. In order to get more compact features vector, the predictive LDA is employed for dimensional reduction. The aims of this research is to develop fast and strong face recognition that can be implemented for electronic key which will be implemented for substituting current security system (PIN and password). In addition, the recognition engine is also designed for real time face recognition which can work on hardware having limited resources such as Intel atom computer, raspberry pi, and android smart phone. The experimental results show that the proposed engine provide high enough recognition rate, and small false rejection rate (FRR) and false acceptance rate (FAR). In addition, the engine needs short processing time.

We present a real-time face tracker in this paper The system has achieved a rate of 30% frameshecond using an HP-9000 workstation with a framegrabber and a Canon VC-CI camera. It can track a person 'sface while the person moves freely (e.g., walks, jumps, sits down and stands up) in a room. Three types of models have been employed in developing the system. First, we present a stochastic model to characterize skin-color distributions of human faces. The information provided by the model is sufJicient for tracking a human face in various poses and views. This model is adaptable to different people and different lighting conditions in real-time. Second, a motion model e's used to estimate image motion and to predict search window. Third, a camera model is used toprediet and to compensate for camera motion. The system can be applied to tele-conferencing and many HCI applications including lip-reading and gaze tracking. The principle in developing this system can be extended to other tracking problems such as tracking the human hand.

In this paper, weinvestigaterecognition of humanfaces in a meetingroom. The major challenges of identifying humanfacesin this environmentincludelow quality of input images,poor illumination,unrestrictedheadposesand continuouslychangingfacial expressionsandocclusion.In order to addresstheseproblemswe proposea novel algorithm, DynamicSpaceWarping (DSW).Thebasic idea of the algorithm is to combinelocal features under certain spatial constraints. We compare DSWwith the eigenface approachondatacollectedfromvariousmeetings.Wehave testedboth front and profile face imagesand imageswith two stagesof occlusion.Theexperimentalresultsindicate thattheDSWapproachoutperformstheeigenfaceapproach in bothcases.

This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.

Proc. of the International Conference on Computer Vision, Corfu (Sept. 1999) An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection. These features share similar properties with neurons in inferior temporal cortex that are used for object recognition in primate vision. Features are efficiently detected through a staged filtering approach that identifies stable points in scale space. Image keys are created that allow for local geometric deformations by representing blurred image gradients in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches. Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.


Question: Detecting facial features: An approach based on facial symmetry.

This paper introduces an innovative approach for pedestrian detection that consolidates multiple high-performing detectors by incorporating an intermediate layer to filter low-level features, coupled with a boosted decision forest. Motivated by this observation, we present a comprehensive framework and conduct experimental investigations into various filter families. Our extensive results facilitate a thorough analysis, demonstrating that by utilizing filtered channel features, we achieve state-of-the-art performance on the demanding Caltech and KITTI datasets, using only HOG+LUV as our low-level features. Remarkably, by integrating optical flow features into our system, we significantly enhance detection accuracy, achieving the highest reported results on the Caltech dataset, with a 93% recall at 1 FPPI.

**Question**: The role of social media in mental health: A systematic review

**Documents**: The effects of social media on college students
The present study aims to explore the effects of social media on college students. We conducted a survey of 200 students from a private university in the United States. The results showed that social media usage has a significant impact on academic performance, social skills, and mental health. Students who spent more time on social media platforms had lower grades, reported feeling more isolated, and experienced higher levels of anxiety and depression. The findings suggest that while social media can be a valuable tool for communication and networking, excessive use can have detrimental effects on mental health and academic success.

**Similar Documents**: This research investigates the influence of social media on the well-being and academic outcomes of college students. A survey was administered to a cohort of 200 students enrolled at a private institution in the U.S., with the objective of understanding the correlation between social media engagement and various aspects of student life. Our analysis revealed that increased social media consumption is closely linked to a decline in academic performance, a sense of social isolation, and elevated instances of anxiety and depression. These results underscore the dual nature of social media: while it serves as an essential platform for connection and information exchange, overreliance on these platforms can lead to adverse impacts on mental health and academic achievements.

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).

Face detection is one of the challenging problems in image processing. This report proposes a novel technique for detecting faces in color images using skin color model algorithm combined with skin likely-hood, skin Segmentation, Morphological operation and Template matching. Color images with skin color in the chromatic and pure color space YCrCb, which separates luminance and chrominance components. A Gaussian probability density is estimated from skin samples, collected from different ethnic groups, via the maximum-likelihood criterion. Adaptive thresholding for segmentation to localize the faces within the detected skin regions. Further, mathematical morphological operators are used to remove noisy regions and fill holes in the skin-color region, so that candidate human face regions can be extracted. These systems can achieve high detection accuracy, high detection speed and reduce the false detecting rate. The two methodology used for the Skin Segmentation is YCbCr and RGB Model. In YCbCr model both the skin colour and texture of the image can be used to identify the particular object in the image,where as in RGB model only the skin color has to be used for identification of the person.Hence the YCbCr model is better than the RGB model . From our analysis we conclude that the new approach in modeling skin color can achieve good detection success rate. The algorithm gives computationally a very efficient as well as an accurate approach for skin detection. The performance of different color space may be dependent on the method used to model the color for skin pixel. Keywords—Gaussian probability, YCrCb,RGB Model

Previous research on facial expression recognition mainly focuses on near frontal face images, while in realistic interactive scenarios, the interested subjects may appear in arbitrary non-frontal poses. In this paper, we propose a framework to recognize six prototypical facial expressions, namely, anger, disgust, fear, joy, sadness and surprise, in an arbitrary head pose. We build a multi-pose training set by rendering 3D face scans from the BU-4DFE dynamic facial expression database [17] at 49 different viewpoints. We extract Local Binary Pattern (LBP) descriptors and further utilize multiple instance learning to mitigate the influence of inaccurate alignment in this challenging task. Experimental results demonstrate the power and validate the effectiveness of the proposed multi-pose facial expression recognition framework.

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.

Facial gender and smile classification in unconstrained environment is challenging due to the invertible and large variations of face images. In this paper, we propose a deep model composed of GNet and SNet for these two tasks. We leverage the multi-task learning and the general-to-specific fine-tuning scheme to enhance the performance of our model. Our strategies exploit the inherent correlation between face identity, smile, gender and other face attributes to relieve the problem of over-fitting on small training set and improve the classification performance. We also propose the tasks-aware face cropping scheme to extract attribute-specific regions. The experimental results on the ChaLearn 16 FotW dataset for gender and smile classification demonstrate the effectiveness of our proposed methods.

Automatic recognition of people has received much attention during the recent years due to its many applications in different fields such as law enforcement, security applications or video indexing. Face recognition is an important and very challenging technique to automatic people recognition. Up to date, there is no technique that provides a robust solution to all situations and different applications that face recognition may encounter. In general, we can make sure that performance of a face recognition system is determined by how to extract feature vector exactly and to classify them into a group accurately. It, therefore, is necessary for us to closely look at the feature extractor and classifier. In this paper, Principle Component Analysis (PCA) is used to play a key role in feature extractor and the SVMs are used to tackle the face recognition problem. Support Vector Machines (SVMs) have been recently proposed as a new classifier for pattern recognition. We illustrate the potential of SVMs on the Cambridge ORL Face database, which consists of 400 images of 40 individuals, containing quite a high degree of variability in expression, pose, and facial details. The SVMs that have been used included the Linear (LSVM), Polynomial (PSVM), and Radial Basis Function (RBFSVM) SVMs. We provide experimental evidence which show that Polynomial and Radial Basis Function (RBF) SVMs performs better than Linear SVM on the ORL Face Dataset when both are used with one against all classification. We also compared the SVMs based recognition with the standard eigenface approach using the Multi-Layer Perceptron (MLP) Classification criterion.

In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison.

The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

A perfect hairstyle enhanced anyone's self-confidence, especially women. However, in order to choose a good hairstyle, one was limited to rely on knowledge of a beauty expert. This paper presented a hairstyle recommendation system for women based on hairstyle experts' knowledge and a face shape classification scheme that the authors devised in a previous study. The system showed a user's face with a recommended or not recommended hairstyle on a monitor.

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.


Question: Facial symmetry: An approach for detecting facial features.

This paper delves into the exploration of feature sets for reliable visual object recognition, specifically focusing on linear SVM-based human detection as a practical application. Through a comparative analysis, we demonstrate that grids of Histograms of Oriented Gradients (HOG) descriptors surpass other existing feature sets in the context of human detection. We conduct a detailed examination of each step in the HOG computation process, revealing that critical factors for optimal performance include the use of fine-scale gradients, precise orientation binning, coarser spatial binning, and effective local contrast normalization within overlapping descriptor blocks. Our novel approach yields exceptional results, achieving near-perfect separation on the MIT pedestrian database. To further test the robustness of our method, we introduce a more demanding dataset comprising over 1800 meticulously annotated human images, encompassing a broad spectrum of poses and diverse backgrounds, thus confirming the superiority of our HOG-based approach in visual object recognition tasks.

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.

This paper describes the representation, animation and data collection techniques that have been used to produce "realistic" computer generated half-tone animated sequences of a human face changing expression. It was determined that approximating the surface of a face with a polygonal skin containing approximately 250 polygons defined by about 400 vertices is sufficient to achieve a realistic face. Animation was accomplished using a cosine interpolation scheme to fill in the intermediate frames between expressions. This approach is good enough to produce realistic facial motion. The three-dimensional data used to describe the expressions of the face was obtained photogrammetrically using pairs of photographs.

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.

The accuracy of automated human face recognition algorithms can significantly degrade while recognizing same subjects under make-up and disguised appearances. Increasing constraints on enhanced security and surveillance requires enhanced accuracy from face recognition algorithms for faces under disguise and/or makeup. This paper presents a new database for face images under disguised and make-up appearances the development of face recognition algorithms under such covariates. This database has 2460 images from 410 different subjects and is acquired under real environment, focuses on make-up and disguises covariates and also provides ground truth (eye glass, goggle, mustache, beard) for every image. This can enable developed algorithms to automatically quantify their capability for identifying such important disguise attribute during the face recognition We also present comparative experimental results from two popular commercial matchers and from recent publications. Our experimental results suggest significant performance degradation in the capability of these matchers in automatically recognizing these faces. We also analyze face detection accuracy from these matchers. The experimental results underline the challenges in recognizing faces under these covariates. Availability of this new database in public domain will help to advance much needed research and development in recognizing make-up and disguised faces.

Local deep neural networks have been recently introduced for gender recognition. Although, they achieve very good performance they are very computationally expensive to train. In this work, we introduce a simplified version of local deep neural networks which significantly reduces the training time. Instead of using hundreds of patches per image, as suggested by the original method, we propose to use 9 overlapping patches per image which cover the entire face region. This results in a much reduced training time, since just 9 patches are extracted per image instead of hundreds, at the expense of a slightly reduced performance. We tested the proposed modified local deep neural networks approach on the LFW and Adience databases for the task of gender and age classification. For both tasks and both databases the performance is up to 1% lower compared to the original version of the algorithm. We have also investigated which patches are more discriminative for age and gender classification. It turns out that the mouth and eyes regions are useful for age classification, whereas just the eye region is useful for gender classification.

In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn. 2008 Published by Elsevier B.V.

This paper concerns the estimation of facial attributes-namely, age and gender-from images of faces acquired in challenging, in the wild conditions. This problem has received far less attention than the related problem of face recognition, and in particular, has not enjoyed the same dramatic improvement in capabilities demonstrated by contemporary face recognition systems. Here, we address this problem by making the following contributions. First, in answer to one of the key problems of age estimation research-absence of data-we offer a unique data set of face images, labeled for age and gender, acquired by smart-phones and other mobile devices, and uploaded without manual filtering to online image repositories. We show the images in our collection to be more challenging than those offered by other face-photo benchmarks. Second, we describe the dropout-support vector machine approach used by our system for face attribute estimation, in order to avoid over-fitting. This method, inspired by the dropout learning techniques now popular with deep belief networks, is applied here for training support vector machines, to the best of our knowledge, for the first time. Finally, we present a robust face alignment technique, which explicitly considers the uncertainties of facial feature detectors. We report extensive tests analyzing both the difficulty levels of contemporary benchmarks as well as the capabilities of our own system. These show our method to outperform state-of-the-art by a wide margin.

Facial expressions are an important way through which humans interact socially. Building a system capable of automatically recognizing facial expressions from images and video has been an intense field of study in recent years. Interpreting such expressions remains challenging and much research is needed about the way they relate to human affect. This paper presents a general overview of automatic RGB, 3D, thermal and multimodal facial expression analysis. We define a new taxonomy for the field, encompassing all steps from face detection to facial expression recognition, and describe and classify the state of the art methods accordingly. We also present the important datasets and the bench-marking of most influential methods. We conclude with a general discussion about trends, important questions and future lines of research.

This paper presents design research conducted as part of a larger project on human-robot interaction. The primary goal of this study was to come to an initial understanding of what features and dimensions of a humanoid robot's face most dramatically contribute to people's perception of its humanness. To answer this question we analyzed 48 robots and conducted surveys to measure people's perception of each robot's humanness. Through our research we found that the presence of certain features, the dimensions of the head, and the total number of facial features heavily influence the perception of humanness in robot heads. This paper presents our findings and initial guidelines for the design of humanoid robot heads.

Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.


Question: Detecting facial features: An approach based on facial symmetry.

This article outlines a novel technique for visual object detection, particularly suited for real-time applications, which excels in both speed and accuracy. The proposed method introduces a unique image representation known as the "Integral Image," enabling rapid computation of the features employed by the detector. A key innovation lies in the learning algorithm, which is based on the AdaBoost framework, allowing the selection of a limited number of critical visual features from a larger pool, resulting in highly efficient classifiers. Another significant contribution is the cascade structure, which combines increasingly complex classifiers, enabling the system to swiftly discard non-object areas while focusing computational resources on regions more likely to contain the object of interest. This cascade can be seen as a mechanism for object-specific attention, distinguishing itself from prior approaches by providing statistical assurances that discarded regions are unlikely to contain the sought-after object. When applied to the domain of face detection, the system demonstrates comparable performance to leading methods, with the notable advantage of running at 15 frames per second without the need for image differencing or reliance on skin color detection, making it a robust and versatile solution for real-time facial feature detection.

Current 2D face recognition systems encounter difficulties in recognizing faces with large pose variations. Utilizing the pose-invariant features of 3D face data has the potential to handle multiview face matching. A feature extractor based on the directional maximum is proposed to estimate the nose tip location and the pose angle simultaneously. A nose profile model represented by subspaces is used to select the best candidates for the nose tip. Assisted by a statistical feature location model, a multimodal scheme is presented to extract eye and mouth corners. Using the automatic feature extractor, a fully automatic 3D face recognition system is developed. The system is evaluated on two databases, the MSU database (300 multiview test scans from 100 subjects) and the UND database (953 near frontal scans from 277 subjects). The automatic system provides recognition accuracy that is comparable to the accuracy of a system with manually labeled feature points

a r t i c l e i n f o Since 2005, human and computer performance has been systematically compared as part of face recognition competitions, with results being reported for both still and video imagery. The key results from these competitions are reviewed. To analyze performance across studies, the cross-modal performance analysis (CMPA) framework is introduced. The CMPA framework is applied to experiments that were part of face a recognition competition. The analysis shows that for matching frontal faces in still images, algorithms are consistently superior to humans. For video and difficult still face pairs, humans are superior. Finally, based on the CMPA framework and a face performance index, we outline a challenge problem for developing algorithms that are superior to humans for the general face recognition problem.

We present a new technique for passive and markerless facial performance capture based on anchor frames. Our method starts with high resolution per-frame geometry acquisition using state-of-the-art stereo reconstruction, and proceeds to establish a single triangle mesh that is propagated through the entire performance. Leveraging the fact that facial performances often contain repetitive subsequences, we identify anchor frames as those which contain similar facial expressions to a manually chosen reference expression. Anchor frames are automatically computed over one or even multiple performances. We introduce a robust image-space tracking method that computes pixel matches directly from the reference frame to all anchor frames, and thereby to the remaining frames in the sequence via sequential matching. This allows us to propagate one reconstructed frame to an entire sequence in parallel, in contrast to previous sequential methods. Our anchored reconstruction approach also limits tracker drift and robustly handles occlusions and motion blur. The parallel tracking and mesh propagation offer low computation times. Our technique will even automatically match anchor frames across different sequences captured on different occasions, propagating a single mesh to all performances.

In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.

In this paper, we propose a weakly supervised landmark-region-based convolutional neural network (LR-CNN) framework to detect facial component and landmark simultaneously. Most of the existing course-to-fine facial detectors fail to detect landmark accurately without lots of fully labeled data, which are costly to obtain. We can handle the task with a small amount of finely labeled data. First, deep convolutional generative adversarial networks are utilized to generate training samples with weak labels, as data preparation. Then, through weakly supervised learning, our LR-CNN model can be trained effectively with a small amount of finely labeled data and a large amount of generated weakly labeled data. Notably, our approach can handle the situation when large occlusion areas occur, as we localize visible facial components before predicting corresponding landmarks. Detecting unblocked components first helps us to focus on the informative area, resulting in a better performance. Additionally, to improve the performance of the above tasks, we design two models as follows: 1) we add AnchorAlign in the region proposal networks to accurately localize components and 2) we propose a two-branch model consisting classification branch and regression branch to detect landmark. Extensive evaluations on benchmark datasets indicate that our proposed approach is able to complete the multi-task facial detection and outperforms the state-of-the-art facial component and landmark detection algorithms.

Human faces undergo considerable amounts of variations with aging. While face recognition systems have been proven to be sensitive to factors such as illumination and pose, their sensitivity to facial aging effects is yet to be studied. How does age progression affect the similarity between a pair of face images of an individual? What is the confidence associated with establishing the identity between a pair of age separated face images? In this paper, we develop a Bayesian age difference classifier that classifies face images of individuals based on age differences and performs face verification across age progression. Further, we study the similarity of faces across age progression. Since age separated face images invariably differ in illumination and pose, we propose preprocessing methods for minimizing such variations. Experimental results using a database comprising of pairs of face images that were retrieved from the passports of 465 individuals are presented. The verification system for faces separated by as many as nine years, attains an equal error rate of 8.5%

The paper describes a novel technique for the recognition of emotions from multimodal data. We focus on the recognition of the six prototypic emotions. The results from the facial expression recognition and from the emotion recognition from speech are combined using a bi-modal multimodal semantic data fusion model that determines the most probable emotion of the subject. Two types of models based on geometric face features for facial expression recognition are being used, depending on the presence or absence of speech. In our approach we define an algorithm that is robust to changes of face shape that occur during regular speech. The influence of phoneme generation on the face shape during speech is removed by using features that are only related to the eyes and the eyebrows. The paper includes results from testing the presented models.

We consider the problem of automatically recognizing human faces from frontal views with varying expression and illumination, as well as occlusion and disguise. We cast the recognition problem as one of classifying among multiple linear regression models and argue that new theory from sparse signal representation offers the key to addressing this problem. Based on a sparse representation computed by C1-minimization, we propose a general classification algorithm for (image-based) object recognition. This new framework provides new insights into two crucial issues in face recognition: feature extraction and robustness to occlusion. For feature extraction, we show that if sparsity in the recognition problem is properly harnessed, the choice of features is no longer critical. What is critical, however, is whether the number of features is sufficiently large and whether the sparse representation is correctly computed. Unconventional features such as downsampled images and random projections perform just as well as conventional features such as eigenfaces and Laplacianfaces, as long as the dimension of the feature space surpasses certain threshold, predicted by the theory of sparse representation. This framework can handle errors due to occlusion and corruption uniformly by exploiting the fact that these errors are often sparse with respect to the standard (pixel) basis. The theory of sparse representation helps predict how much occlusion the recognition algorithm can handle and how to choose the training images to maximize robustness to occlusion. We conduct extensive experiments on publicly available databases to verify the efficacy of the proposed algorithm and corroborate the above claims.

We present a real-time performance-driven facial animation system based on 3D shape regression. In this system, the 3D positions of facial landmark points are inferred by a regressor from 2D video frames of an ordinary web camera. From these 3D points, the pose and expressions of the face are recovered by fitting a user-specific blendshape model to them. The main technical contribution of this work is the 3D regression algorithm that learns an accurate, user-specific face alignment model from an easily acquired set of training data, generated from images of the user performing a sequence of predefined facial poses and expressions. Experiments show that our system can accurately recover 3D face shapes even for fast motions, non-frontal faces, and exaggerated expressions. In addition, some capacity to handle partial occlusions and changing lighting conditions is demonstrated.

Improved dense trajectory features have been successfully used in video-based action recognition problems, but their application to face processing is more challenging. In this paper, we propose a novel system that deals with the problem of emotion recognition in real-world videos, using improved dense trajectory, LGBP-TOP, and geometric features. In the proposed system, we detect the face and facial landmarks from each frame of a video using a combination of two recent approaches, and register faces by means of Procrustes analysis. The improved dense trajectory and geometric features are encoded using Fisher vectors and classification is achieved by extreme learning machines. We evaluate our method on the extended Cohn-Kanade (CK+) and EmotiW 2015 Challenge databases. We obtain state-of the-art results in both databases.


Question: An intelligent system for detecting accidents.

An Innovative Vehicle Accident Detection and Emergency Response System Utilizing GSM and GPS Technology

The rapid advancement of technology and infrastructure has significantly enhanced our daily lives. However, this progress has also led to increased traffic hazards and frequent road accidents, resulting in significant loss of life and property due to inadequate emergency response systems. Our proposed solution aims to address this critical issue by implementing an intelligent accident detection system.

Incorporating an accelerometer in a car's alarm system enables the detection of dangerous driving patterns, acting as a crash or rollover detector. The sensor's signals can identify severe accidents, triggering an immediate response. Upon detecting a collision or rollover, the vibration sensor activates, and the Micro Electro Mechanical System (MEMS) sensor relays the signal to the ARM controller. Subsequently, the microcontroller sends an alert message, including the vehicle's location, via a GSM modem to the police control room or an emergency response team.

Upon receiving the alert, the police can swiftly locate the vehicle using the GPS modem, allowing for prompt action to be taken. To prevent unnecessary emergency mobilization in minor accidents, a switch is provided for the driver to terminate the alert manually, ensuring the efficient allocation of emergency resources.

This system's effectiveness is bolstered by its dual-sensor approach, combining the capabilities of both vibration and MEMS sensors for enhanced accident detection accuracy. Looking forward, the system's potential for improvement includes the integration of a wireless webcam, capable of capturing images that can provide additional insights and support driver assistance in emergency situations. This innovative approach not only ensures timely emergency response but also optimizes resource utilization, significantly enhancing the safety and well-being of drivers and passengers.

In this paper, an attempt has been made to develop a smart device that can assist women when they feel unsafe. This smart device will be clipped to the footwear of the user and can be triggered discreetly. On tapping one foot behind the other four times, an alert is sent via Bluetooth Low Energy communication to an application on the victim's phone, programmed to generate a message seeking help with the location of the device attached. The results obtained were analysed using Naïve Bayes classifier and this low cost device showed an overall accuracy of 97.5%.

This paper aims to develop a convenient traffic system that allows a smooth movement of cars which will help build a smarter city. The traffic system currently implemented in many areas is not based on the density of traffic and every road is allotted a preset time. This results in traffic congestion due to large red-light delays and timings allotted for roads in a city that should vary during peak on-off hours, but in reality don't. These traditional systems are not adaptable and fail to support traffic during an unexpected situation or during an accident, and this makes them inefficient. In order to calculate the density of traffic various sensors can be used, each having their merits and demerits. In our proposed system Ultrasound Sensors are used along with Image Processing(using live feed from a camera) that works on a Raspberry Pi platform and calculates the vehicle density and dynamically allots time for different levels of traffic. This in turn allows better signal control and effective management of traffic thereby reducing the probability of a collision. By using Internet Of Things(IoT) real time data from the system can be collected, stored and managed on a cloud. This data can be used to interpret the signal duration in-case any of the sensing equipment fail, and also for future analysis.

Advanced Driver Assistance Systems have been shown to greatly improve road safety. However, existing systems are typically reactive with an inability to understand complex traffic scenarios. We present a method to predict driver intention as the vehicle enters an intersection using a Long Short Term Memory (LSTM) based Recurrent Neural Network (RNN). The model is learnt using the position, heading and velocity fused from GPS, IMU and odometry data collected by the ego-vehicle. In this paper we focus on determining the earliest possible moment in which we can classify the driver's intention at an intersection. We consider the outcome of this work an essential component for all levels of road vehicle automation.

The advanced sensing and imaging capability of today's sensor networks enables real time monitoring in a large area. In order to provide continuous monitoring and prompt situational awareness, an abstract-level autonomous information processing framework is developed that is able to detect various categories of abnormal traffic events with unsupervised learning. The framework is based on cogent confabulation model, which performs statistical inference in a manner inspired by human neocortex system. It enables detection and recognition of abnormal target vehicles within the context of surrounding traffic activities and previous events using likelihood-ratio test. A neuromorphic architecture is proposed which accelerates the computation for real-time detection by leveraging memristor crossbar arrays.

In recent years, many researches on Intelligent Transportation Systems (ITS) have been reported. Automatic License Plate Recognition (ALPR) is one form of ITS technology that not only recognizes and counts vehicles, but distinguishes each as unique by recognizing and recording the license plate’s characters. This paper discusses the main techniques of ALPR. Several open problems are proposed at the end of the paper for future research.

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intrusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

In a typical surveillance installation, a human operator has to constantly monitor a large array of video feeds for suspicious behaviour. As the number of cameras increases, information overload makes manual surveillance increasingly difficult, adding to other confounding factors such as human fatigue and boredom. The objective of an intelligent vision-based surveillance system is to automate the monitoring and event detection components of surveillance, alerting the operator only when unusual behaviour or other events of interest are detected. While most traditional methods for trajectory-based unusual behaviour detection rely on low-level trajectory features such as flow vectors or control points, this paper builds upon a recently introduced approach that makes use of higher-level features of intentionality. Individuals in the scene are modelled as intentional agents, and unusual behaviour is detected by evaluating the explicability of the agent’s trajectory with respect to known spatial goals. The proposed method extends the original goal-based approach in three ways: first, the spatial scene structure is learned in a training phase; second, a region transition model is learned to describe normal movement patterns between spatial regions; and third, classification of trajectories in progress is performed in a probabilistic framework using particle filtering. Experimental validation on three published third-party datasets demonstrates the validity of the proposed approach.

Firewalls are now an integral part of network security. An intelligent firewall that prevents unauthorized access to a system has been developed. Artificial intelligence applications are uniquely suited for the ever-changing, ever-evolving world of network security. Typical firewalls are only as good as the information provided by the Network Administrator. A new type of attack creates vulnerabilities, which a static firewall does not have the ability to avoid without human direction. An AI-managed firewall service, however, can protect a computer network from known and future threats. We report in this paper on research in progress concerning the integration of different security techniques. A main purpose of the project is to integrate a smart detection engine into a firewall. The smart detection engine will aim at not only detecting anomalous network traffic as in classical IDSs, but also detecting unusual structures data packets that suggest the presence of virus data. We will report in this paper on the concept of an intelligent firewall that contains a smart detection engine for potentially malicious data packets.

A driver assistance system (DAS) should support the driver by monitoring road and vehicle events and presenting relevant and timely information to the driver. It is impossible to know what a driver is thinking, but we can monitor the driver’s gaze direction and compare it with the position of information in the driver’s viewfield to make inferences. In this way, not only do we monitor the driver’s actions, we monitor the driver’s observations as well. In this paper we present the automated detection and recognition of road signs, combined with the monitoring of the driver’s response. We present a complete system that reads speed signs in real-time, compares the driver’s gaze, and provides immediate feedback if it appears the sign has been missed by t ©

Driver behavior affects traffic safety, fuel/energy consumption and gas emissions. The purpose of driver behavior profiling is to understand and have a positive influence on driver behavior. Driver behavior profiling tasks usually involve an automated collection of driving data and the application of computer models to classify what characterizes the aggressiveness of drivers. Different sensors and classification methods have been employed for this task, although low-cost solutions, high performance and collaborative sensing remain open questions for research. This paper makes an investigation with different Recurrent Neural Networks (RNN), aiming to classify driving events employing data collected by smartphone accelerometers. The results show that specific configurations of RNN upon accelerometer data provide high accuracy results, being a step towards the development of safer transportation systems.


Question: An intelligent system for detecting accidents.

An Advanced Accident Detection System Utilizing GPS and GSM Modem Technology

With the rapid rise in vehicle usage due to technological advancements and population growth, the frequency of accidents has escalated, leading to a pressing need to prioritize human life. While accidents are inevitable, timely medical attention can significantly increase survival rates. To address this, an innovative system, the Intelligent Transportation System (ITS), has been introduced with the primary goal of reducing delays caused by traffic congestion and ensuring the swift passage of emergency vehicles.

The core functionality of ITS is centered around automatically prioritizing traffic signals for ambulances using a radio frequency (RF) module. This mechanism ensures that the ambulance can swiftly reach the accident site, thereby saving lives. Upon an accident, ITS immediately identifies its location and alerts the main server, which then dispatches the nearest ambulance to the scene. The system's control unit continuously monitors the ambulance's progress and dynamically provides the shortest route to the hospital, while simultaneously coordinating traffic light operations to ensure a clear path.

This automated system is designed to accurately pinpoint accident locations, efficiently manage traffic, and guide emergency vehicles to their destination, thereby ensuring the safety and timely medical care of victims. By integrating GPS and GSM modem technologies, ITS represents a significant advancement in accident detection and response systems.

This paper proposes and validates a real-time on-road vehicle detection system, which uses a single camera for the purpose of intelligent driver assistance. A three-step vehicle detection framework is presented to detect and track the target vehicle within an image. In the first step, probable vehicle locations are hypothesized using pattern recognition. The vehicle candidates are then verified in the hypothesis verification step. In this step, lane detection is used to filter vehicle candidates that are not within the lane region of interest. In the final step tracking and online learning are implemented to optimize the detection algorithm during misdetection and temporary occlusion. Good detection performance and accuracy was observed in highway driving environments with minimal shadows.

The demand for reliable traffic sign recognition (TSR) increases with the development of safety driven advanced driver assistance systems (ADAS). Emerging technologies like brake-by-wire or steer-by-wire pave the way for collision avoidance and threat identification systems. Obviously, decision making in such critical situations requires high reliability of the information base. Especially for comfort systems, we need to take into account that the user tends to trust the information provided by the ADAS [1]. In this paper, we present a robust system architecture for the reliable recognition of circular traffic signs. Our system employs complementing approaches for the different stages of current TSR systems. This introduces the application of local SIFT features for content-based traffic sign detection along with widely applied shape-based approaches. We further add a technique called contracting curve density (CCD) to refine the localization of the detected traffic sign candidates and therefore increase the performance of the subsequent classification module. Finally, the recognition stage based on SIFT and SURF descriptions of the candidates executed by a neural net provides a robust classification of structured image content like traffic signs. By applying these steps we compensate the weaknesses of the utilized approaches, and thus, improve the system's performance.

Recently automotive embedded system has developed highly since the advent of smart car, electric card and so on. They have various value-added system for example IPA (Intelligent Parking Assistance), BSW (Blind Spot Warning), LDWS (Lane Departure warning System), LKS(Lane Keeping System)-these are ADAS (Advanced Driver Assistance Systems). AUTOSAR (AUTomotive Open System Architecture) is the most notable industrial standard for developing automotive embedded software. AUTOSAR is a partnership of automotive manufacturers and suppliers working together to develop and establish an open industry standard for automotive E/E architectures. In this paper, we will introduce AUTOSAR briefly and demonstrate the result of automotive software LDWS (Lane Detection & Warning System) development.

This paper proposes a reference architecture to increase reliability and robustness of an automated vehicle. The architecture exploits the benefits arising from the in-terdependencies of the system and provides self awareness. Performance Assessment units attached to subsystems quantify the reliability of their operation and return performance values. The Environment Condition Assessment, which is another important novelty of the architecture, informs augmented sensors on current sensing conditions. Utilizing environment conditions and performance values for subsequent centralized integrity checks allow algorithms to adapt to current driving conditions and thereby to increase their robustness. We demonstrate the benefit of the approach with the example of false positive object detection and tracking, where the detection of a ghost object is resolved in centralized performance assessment using a Bayesian network.

Recognizing driver awareness is an important prerequisite for the design of advanced automotive safety systems. Since visual attention is constrained to a driver's field of view, knowing where a driver is looking provides useful cues about his activity and awareness of the environment. This work presents an identity-and lighting-invariant system to estimate a driver's head pose. The system is fully autonomous and operates online in daytime and nighttime driving conditions, using a monocular video camera sensitive to visible and near-infrared light. We investigate the limitations of alternative systems when operated in a moving vehicle and compare our approach, which integrates Localized Gradient Orientation histograms with support vector machines for regression. We estimate the orientation of the driver's head in two degrees-of-freedom and evaluate the accuracy of our method in a vehicular testbed equipped with a cinematic motion capture system.

The focus of this paper is on surveying UAV-based systems for traffic monitoring and management. Although there has been voluminous research on the subject, unmanned aerial vehicles (UAVs) are proven to be a viable and less time-consuming alternative to real-time traffic monitoring and management, providing the eye-in-the-sky solution to the problem.

This paper outlines a bus detection mechanism for the blind in travelling from one place to another. In order to get transportation independently, the blind use auditory touched clues like walking stick or white cane. The limitation of the walking stick is that a blind person must come into close proximity with their surroundings to determine the location of an obstacle. For that basis, various devices have been developed such as the Sonicguide, the Mowat sensor, the Laser cane and the Navbelt [4]. However, these device can only assist the blind at a pedestrian crossing. Therefore, the project is aims to develop a bus detection prototype using Radio Frequency Identification (RFID) for the blind. The paper covers brief idea about the blind and RFID system, review relevant papers and summary of current research. The review of RFID system compare between families of auto-ID technology, the basic principle of RFID, the type of RFID tagging and the antenna characteristic. The summary of current research discussed about the development of prototype, the database system, the output mechanism and integration between hardware and software. Database management will provided. The information such as bus route, final destination and bus number are also provided. This paper also describes the future work intended to be done.

This article proposes a system that fuses radar and monocular vision sensor data in order to detect and classify on-road obstacles, like cars or not cars (other obstacles). The obstacle detection process and classification is divided into three stages, the first consist in reading radar signals and capturing the camera data, the second stage is the data fusion, and the third step is the classify the obstacles, aiming to differentiate the obstacles types identified by the radar and confirmed by the computer vision. In the detection task it is important to locate, measure, and rank the obstacles to be able to take adequate decisions and actions (e.g. Generate alerts, autonomously control the vehicle path), so the correct classification of the obstacle type and position, is very important, also avoiding false positives and/or false negatives in the classification task.

Detecting anomalies such as rule violations, accidents, unusual driving and other suspicious action increase the need for automatic analysis in Traffic Video Surveillance (TVS). Most of the works in Traffic rule violation systems are based on probabilistic methods of classification for detecting the events as normal and abnormal. This paper proposes an un-supervised clustering technique namely Novel Anomaly Detection-Density Based Spatial Clustering of Applications with Noise (NAD-DBSCAN) which clusters the trajectories of moving objects of varying sizes and shapes. A trajectory is said to be abnormal if the event that never fit with the trained model. Epsilon (Eps) and Minimum Points (MinPts) are essential parameters for dynamically calculating the sum of clusters for a data point. The proposed system is validated using benchmark traffic dataset and found to perform accurately in detecting anomalies.

Vehicular ad hoc networking (VANET) have become a significant technology in the current years because of the emerging generation of self-driving cars such as Google driverless cars. VANET have more vulnerabilities compared to other networks such as wired networks, because these networks are an autonomous collection of mobile vehicles and there is no fixed security infrastructure, no high dynamic topology and the open wireless medium makes them more vulnerable to attacks. It is important to design new approaches and mechanisms to rise the security these networks and protect them from attacks. In this paper, we design an intrusion detection mechanism for the VANETs using Artificial Neural Networks (ANNs) to detect Denial of Service (DoS) attacks. The main role of IDS is to detect the attack using a data generated from the network behavior such as a trace file. The IDSs use the features extracted from the trace file as auditable data. In this paper, we propose anomaly and misuse detection to detect the malicious attack.


Question: An intelligent system for detecting accidents.

This paper details the creation of a vehicle tracking solution that harnesses the capabilities of GPS and GSM technology, addressing the growing demand for enhanced vehicle security and management systems. Given the escalating number of vehicles worldwide and the myriad applications of vehicle tracking, such as personal vehicle safety, public transport enhancement, and fleet administration, the necessity for an efficient and user-friendly vehicle tracking mechanism is evident. Our project aims to construct a vehicle tracking system that allows users to effortlessly locate their vehicles in real-time, leveraging the ubiquitous coverage of mobile networks. The paper outlines the development of a hardware prototype for this system, focusing on its ability to provide remote tracking through the GSM network. Utilizing a u-blox NEO-6Q GPS receiver for pinpointing a vehicle's location and a u-blox LEON-G100 GSM module for data transmission, the system communicates the vehicle's coordinates directly to the user's mobile device. The system's core components, including the Arduino Uno microcontroller, ensure seamless integration and functionality. The developed vehicle tracking system not only supports near real-time tracking but also offers superior customizability, global compatibility, and cost-effectiveness compared to current market offerings, showcasing its potential to revolutionize the field of vehicle tracking and management.

It plays an important role to accurately track multiple vehicles in intelligent transportation, especially in intelligent vehicles. Due to complicated traffic environments it is difficult to track multiple vehicles accurately and robustly, especially when there are occlusions among vehicles. To alleviate these problems, a new approach is proposed to track multiple vehicles with the combination of robust detection and two classifiers. An improved ViBe algorithm is proposed for robust and accurate detection of multiple vehicles. It uses the gray-scale spatial information to build dictionary of pixel life length to make ghost shadows and object’s residual shadows quickly blended into the samples of the background. The improved algorithm takes good post-processing method to restrain dynamic noise. In this paper, we also design a method using two classifiers to further attack the problem of failure to track vehicles with occlusions and interference. It classifies tracking rectangles with confidence values between two thresholds through combining local binary pattern with support vector machine (SVM) classifier and then using a convolutional neural network (CNN) classifier for the second time to remove the interference areas between vehicles and other moving objects. The two classifiers method has both time efficiency advantage of SVM and high accuracy advantage of CNN. Comparing with several existing methods, the qualitative and quantitative analysis of our experiment results showed that the proposed method not only effectively removed the ghost shadows, and improved the detection accuracy and real-time performance, but also was robust to deal with the occlusion of multiple vehicles in various traffic scenes.

Reliable and efficient perception and reasoning in dynamic and densely cluttered environments are still major challenges for driver assistance systems. Most of today’s systems use target tracking algorithms based on object models. They work quite well in simple environments such as freeways, where few potential obstacles have to be considered. However, these approaches usually fail in more complex environments featuring a large variety of potential obstacles, as is usually the case in urban driving situations. In this paper, we propose a new approach for robust perception and risk assessment in highly dynamic environments. This approach is called Bayesian occupancy filtering; it basically combines a four-dimensional occupancy grid representation of the obstacle state space with Bayesian filtering techniques. KEY WORDS—multitarget tracking, Bayesian state estimation, occupancy grid

Increased driver stress is generally recognized as one of the major factors leading to road accidents and loss of life. Even though physiological signals are reported as the most reliable means to measure driver stresses, they often require the use of unique and expensive sensors, which produce dynamic and varying readings within individuals. This paper presents a novel means to predict a driver’s stress level by evaluating the movement pattern of the steering wheel. This is accomplished by using an inertial motion unit sensor, which is placed on a glove worn by the driver. The motion sensor selected for this paper was chosen because for its low cost and the fact that it is least affected by environmental factors as compared with a physiological signal. Experiments were conducted in three different environmental scenarios. The scenarios were classified as “urban,” “highway,” and “rural,” and they were chosen to simulate contrasting stress conditions experienced by the driver. In this paper, skin conductance and driver self-reports served as a reference stress to predict the driver’s stress level. Galvanic skin response, a well-known stress indicator, was captured along the driver’s palm and the readings were transmitted to a mobile device via low energy Bluetooth for further processing. The results revealed that indirect measurement of steering wheel movement with an inertial motion sensor could obtain accuracies up to an average rate of 94.78%. This demonstrates the opportunity for inclusion of motion sensors in wireless driver assistance systems for ambulatory monitoring of stress levels.

The paper describes the mobile information and communication aspects of a next generation train maintenance and diagnosis system, discusses the working prototype features, and research results. Wearable/ Mobile computers combined with the wireless technology improve efficiency and accuracy of the maintenance work. This technology enables maintenance personnel at the site to communicate with a remote helpdesk / expertise center through digital data, audio, and image.

The percentage of the population with visual impairments is increasing rapidly. Every year, the number of visually impaired people grows by up to 2 million worldwide. The World Health Organization (WHO, 2011) estimates that there are 39 million blind people and 246 million visually impaired people internationally. In addition, visual impairments are closely associated with aging. About 65% of the visually impaired are 50 years of age or older, with about 20% of the world’s population in this age group (WHO, 2011). In Europe, in particular, about 90% of visually impaired and partially sighted people are over the age of 60. One of the most severe difficulties for the visually impaired is safe independent mobility. Having independent mobility is a significant factor in ensuring that this aging group can perform simple daily tasks without depending on others. Clark-Carter (1985) indicates that visually impaired people have a low level of mobility. Further, a recent survey in France revealed that only 2% of the overall visually impaired population uses mobility aids (Sander, Lelievre, & Tallec, 2005). One model of an independent mobility aid for the visually impaired is the traditional white cane, which is inexpensive, lightweight and foldable. Unfortunately, white cane users have difficulty detecting protruding bars or moving vehicles until they are dangerously close, which can lead to collisions and falls. The limited capability of the white cane corresponds to its length and a user’s maneuvering skills. As such, users rarely detect overhanging obstacles at head-level or ranges further than approximately 1 m from the user. Manduchi and Kurniawan (2011) report in a recent survey of 300 visually impaired people that over 40% of the respondents experienced head-level accidents at least once a month. Further, Clark-Carter (1985) found that visually impaired people were shown to be potentially dangerous when using a white cane. To address these difficulties, many “smart” products for the visually impaired have been introduced in the last four decades, including smart canes and handheld or wearable devices that are equipped with a sensor system. A smart cane offers an improvement over a traditional white cane because it has the ability to detect objects above the cane and up to a range of 2 m away using an ultrasonic sensor. A white cane allows objects to be sensed through touch and echolocation from tapping. A smart cane has the same capabilities, except that it uses vibrotactile information and produces vibration alerts for obstacles in front of the user. However, these smart products have not been successfully adopted and used by a large number of visually impaired people (Roentgen, Gelderblom, Soede, & de Witte, 2008). Several ORIGINAL ARTICLE

INTRODUCTION
Driver drowsiness is a significant contributing factor to road crashes. One approach to tackling this issue is to develop technological countermeasures for detecting driver drowsiness, so that a driver can be warned before a crash occurs.

METHOD
The goal of this review is to assess, given the current state of knowledge, whether vehicle measures can be used to reliably predict drowsiness in real time.

RESULTS
Several behavioral experiments have shown that drowsiness can have a serious impact on driving performance in controlled, experimental settings. However, most of those studies have investigated simple functions of performance (such as standard deviation of lane position) and results are often reported as averages across drivers, and across time.

CONCLUSIONS
Further research is necessary to examine more complex functions, as well as individual differences between drivers.

IMPACT ON INDUSTRY
A successful countermeasure for predicting driver drowsiness will probably require the setting of multiple criteria, and the use of multiple measures.

Recently, with the explosive increase of automobiles in cities, parking problems are serious and even worsen in many cities. This paper proposes a new algorithm for parking occupancy detection based on the use of anisotropic magnetoresistive sensors. Parking occupancy detection is abstracted as binary pattern recognition problem. According to the status of the parking space, the recognition result contains two categories: vacant and occupied. The feature extraction method of the parking magnetic signal is proposed. In addition, the classification criteria are derived based on the distance discriminate analysis method. Eighty-two sensor nodes are deployed on the roadside parking spaces. By running the system for six months, we observed that the accuracy rate of the proposed parking occupancy detection algorithm is better than 98%.

At present automobiles upgrades with different technologies for assisting and providing safety for the occupant. Due to this a system known as Automatic Driver Assistance System (ADAS) proposed many systems which aim to help the driver in controlling the vehicle and to drive safely during night time. One of the systems of ADAS is Glare Free High Beam (GFHB) control. This provides the information of the oncoming vehicles and preceding vehicles based on camera, which slices out the high beam and shades spot when it detects the object by avoiding the glare and provides the driver with greater visibility. It makes use of the various sensors, actuator, image processor and microcontroller to detect an object. In this paper, unit level testing is done for different cases in Embedded C, simulated in Visual Studio and results are displayed using GUI.

People with quadriplegia recruit the interest of researchers in introducing automated movement systems for adopted special purpose wheelchairs. These systems were introduced for easing the movement of such type of disabled people independently. This paper proposed a comprehensive control system that can control the movement of Quadriplegia wheelchairs using gaze direction and blink detection. The presented system includes two main parts. The first part consists of a smartphone that applies the propose gaze direction detection based mobile application. It then sends the direction commands to the second part via Wi-Fi connection. The second part is a prototype representing the quadriplegia wheelchair that contains robotic car (two-wheel driving car), Raspberry Pi III and ultrasound sensors. The gaze direction commands, sent from the smartphone, are received by the Raspberry Pi for processing and producing the control signals. The ultrasound sensors are fixed at the front and back of the car for performing the emergency stop when obstacles are detected. The proposed system is based on gaze tracking and direction detection without the requirement of calibration with additional sensors and instruments. The obtained results show the superior performance of the proposed system that proves the claim of authors. The accuracy ratio is ranged between 66% and 82% depending on the environment (indoor and outdoor) and surrounding lighting as well as the smart phone type. Keywords—Gaze direction detection; mobile application; obstacle detection; quadriplegia; Raspberry Pi microcomputer

This report reviews existing fatigue detection and prediction technologies. Data regarding the different technologies available were collected from a wide variety of worldwide sources. The first half of this report summarises the current state of research and development of the technologies and summarises the status of the technologies with respect to the key issues of sensitivity, reliability, validity and acceptability. The second half evaluates the role of the technologies in transportation, and comments on the place of the technologies vis-a-vis other enforcement and regulatory frameworks, especially in Australia and New Zealand. The report authors conclude that the hardware technologies should never be used as the company fatigue management system. Hardware technologies only have the potential to be a last ditch safety device. Nevertheless, the output of hardware technologies could usefully feed into company fatigue management systems to provide real time risk assessment. However, hardware technology output should never be the only input into a management system. Other inputs should at least come from validated software technologies, mutual assessment of fitness for duty and other risk assessments of work load, schedules and rosters. Purpose: For information: to provide an understanding of the place of fatigue detection and prediction technologies in the management of fatigue in drivers of heavy vehicles.