Question: FingerCode: A filterbank technique for the representation and matching of fingerprints.

This document introduces a multichannel methodology for fingerprint classification, an essential indexing strategy in large fingerprint databases. By employing an accurate and consistent classification technique, the time required for fingerprint matching can be significantly reduced. The proposed algorithm achieves a classification accuracy surpassing those reported in previous studies. Fingerprints are categorized into five distinct types: whorl, right loop, left loop, arch, and tented arch. Utilizing a unique representation scheme, FingerCode, and a two-stage classification approach, the algorithm demonstrates its efficacy. It has been rigorously tested on 4,000 images from the NIST-4 database. For the five-class classification challenge, the algorithm achieves an accuracy of 90 percent (with a 1.8 percent rejection rate during feature extraction). When dealing with a four-class problem (combining arch and tented arch into a single category), the accuracy rises to 94.8 percent (with the same 1.8 percent rejection rate). By integrating a reject option at the classification stage, the accuracy can be further enhanced to 96 percent for the five-class task and 97.8 percent for the four-class task, albeit with a total rejection rate of 32.5 percent of the images.

A biometrie based automatic teller machine which works with a two-tier security using voice and fingerprint recognition can help users which are visually challenged, allowing them to use the machine using only their biometric characteristics. An automated teller machine (ATM) requires a user to pass an identity test using their PIN before doing any financial transactions. The current method available for access control in ATM is based on cards and pins which increases the issues of unauthorized access on accounts via card skimming. It is eminently difficult to avoid another person from attaining and using someone else's card also regular smartcards can be lost, duplicated, stolen or falsified with accuracy. Another concern is the accessibility of ATM to differently abled people. These concerns can be overcome by using fingerprint recognition for authentication, as discussed from the researchers' previous study, and by adding up an additional voice recognition system feature as discussed on this paper. The four fingerprint sample patterns of an individual are completely separate and uncorrelated. The action of fingerprint recognition involves pre-processing, feature extraction and minutiae matching. Matching is done by comparing the user's fingerprint with the existing fingerprint database, images which were acquired at the time of opening an account in the bank account. Once the fingerprint of the user passes the authentication procedures of the system the user is now able to carry out further transactions using voice-based commands by speaking through a microphone. This model not only provides security but also accessibility to certain sections of the population like people with visual impairment and eye disabilities. The system uses biometric based user recognition. The authenticity of the account will be checked by the input of the user's fingerprint, this will then allow further transaction via voice recognition that implements a MFCC, DWT and VQ to continue. ATM users are identified by the lowest VQ distortion of each voice input. Out of 50 different legitimate user trials, 42 tries were identified while 8 legitimate user tries were denied of access in the system producing 84% accuracy.

The primary problem dealt with in this paper is the following. Given some description of a visual object, find that object in an actual photograph. Part of the solution to this problem is the specification of a descriptive scheme, and a metric on which to base the decision of "goodness" of matching or detection.

The problem of compressing a large collection of feature vectors is investigated, so that object identification can be processed on the compressed form of the features. The idea is to perform matching of a query image against an image database, using directly the compressed form of the descriptor vectors, without decompression. Specifically, we concentrate on the Scale Invariant Feature Transform (SIFT), a known object detection method, as well as on Dense SIFT and PHOW features, that contain, for each image, about 300 times as many vectors as the original SIFT. Given two feature vectors, we suggest achieving our goal by compressing them using a lossless encoding by means of a Fibonacci code, for which the pairwise matching can be done directly on the compressed files. In our experiments, this approach improves the processing time and incurs only a small loss in compression efficiency relative to standard compressors requiring a decoding phase.

Biometric systems that use a single biometric trait have to contend with noisy data, restricted degrees of freedom, failureto-enroll problems, spoof attacks, and unacceptable error rates. Multibiometric systems that use multiple traits of an individual for authentication, alleviate some of these problems while improving verification performance. We demonstrate that the performance of multibiometric systems can be further improved by learning user-specific parameters. Two types of parameters are considered here. (i) Thresholds that are used to decide if a matching score indicates a genuine user or an impostor, and (ii) weights that are used to indicate the importance of matching scores output by each biometric trait. User-specific thresholds are computed using the cumulative histogram of impostor matching scores corresponding to each user. The user-specific weights associated with each biometric are estimated by searching for that set of weights which minimizes the total verification error. The tests were conducted on a database of 50 users who provided fingerprint, face and hand geometry data, with 10 of these users providing data over a period of two months. We observed that user-specific thresholds improved system performance by ∼ 2%, while user-specific weights improved performance by ∼ 3%.

This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.

The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.

Authentication is a crucial consideration when securing data or any kind of information system. Though existing approaches for authentication are user-friendly, they have vulnerabilities such as the possibility & criminally threatening a user. We propose a novel approach which uses Electroencephalogram (EEG) brain signals for an authentication process. Unique features of EEG data for distinguishing brain activities can potentially be used to authenticate a user. Compared to other biometric systems, this approach is very robust and more secure because response is significantly changed according to instantaneous mental condition. In the proposed approach, the system user is asked to visualize a number while corresponding EEG signals are captured. Captured signals are used to train the system, compared in the authentication process. This approach mainly focuses on the 8 to 30 Hz Alpha and Beta combined frequency band across all EEG channels, since it is the most appropriate band for EEG signals in Brain Computer Interfaces (BCI). Common Spatial Patterns (CSP) values were used as main features to train the model. Linear discriminant analysis (LDA) was used as a classification algorithm for a given set of user data. A trained set of models for each user is embedded in the system as a parameter database. Each user selects the profile attached to their trained model before the authentication session. Eventually, a trained model can authenticate a user after memorization of pre-determined four digits which the user is asked to think at the first stage of the process. The maximum accuracy recorded with the existing data was 96.97%.

It usually takes a fusion of image processing and machine learning algorithms in order to build a fully-functioning computer vision system for hand gesture recognition. Fortunately, the complexity of developing such a system could be alleviated by treating the system as a collection of multiple sub-systems working together, in such a way that they can be dealt with in isolation. Machine learning need to feed on thousands of exemplars (e.g. images, features) to automatically establish some recognisable patterns for all possible classes (e.g. hand gestures) that applies to the problem domain. A good number of exemplars helps, but it is also important to note that the efficacy of these exemplars depends on the variability of illumination conditions, hand postures, angles of rotation, scaling and on the number of volunteers from whom the hand gesture images were taken. These exemplars are usually subjected to image processing first, to reduce the presence of noise and extract the important features from the images. These features serve as inputs to the machine learning system. Different sub-systems are integrated together to form a complete computer vision system for gesture recognition. The main contribution of this work is on the production of the exemplars. We discuss how a dataset of standard American Sign Language (ASL) hand gestures containing 2425 images from 5 individuals, with variations in lighting conditions and hand postures is generated with the aid of image processing techniques. A minor contribution is given in the form of a specific feature extraction method called moment invariants, for which the computation method and the values are furnished with the dataset.

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Nowadays, researchers are paying increasing attention to embedding systems. Cost reduction has lead to an increase in the number of platforms supporting the operating system Linux, jointly with the Raspberry Pi motherboard. Thus, embedding devices on Raspberry-Linux systems is a goal in order to make competitive commercial products. This paper presents a low-cost fingerprint recognition system embedded into a Raspberry Pi with Linux.


Question: FingerCode: A filterbank technique for fingerprint representation and matching.

This paper offers an extensive review of the research conducted in the automatic recognition and analysis of human faces and facial expressions, a task that humans perform effortlessly but poses significant challenges for automated systems. The underlying subproblems include detecting a pattern as a face, identifying the face, analyzing facial expressions, and classifying based on the physical features of the face. The development of a system capable of performing these operations holds vast potential, with applications ranging from criminal identification to authentication in secure systems. Historically, much of the research has focused on identification. Here, we summarize the advancements and methodologies employed in tackling these issues, reflecting on the capabilities of the human visual system as a benchmark for automated systems. Additionally, we introduce novel approaches to these problems, shedding light on face detection, identification, expression analysis, and feature classification.
- Face Detection: Identifying the presence of a face within an image or video frame.
- Face Identification: Recognizing and verifying the identity of an individual based on their facial features.
- Facial Expressions: Analyzing the emotional state or expression conveyed by the face.
- Classification: Categorizing faces based on specific physical attributes or characteristics.
- Facial Features: Extracting and utilizing distinctive elements of the face for recognition and analysis.

Smoothing fingerprint ridge orientation involves a principal discrepancy. Too little smoothing can result in noisy orientation fields (OF), too much smoothing will harm high curvature areas, especially singular points (SP). In this paper we present a fingerprint ridge orientation model based on Legendre polynomials. The motivation for the proposed method can be found by analysing the almost exclusively used method in literature for orientation smoothing, proposed by Witkin and Kass (1987) more than two decades ago. The main contribution of this paper argues that the vectorial data (sine and cosine data) should be smoothed in a coupled way and the approximation error should not be evaluated employing vectorial data. For evaluating the proposed method we use a Poincare-Index based SP detection algorithm. The experiments show, that in comparison to competing methods the proposed method has improved orientation smoothing capabilities, especially in high curvature areas.

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.

In this paper, a generic texture descriptor, namely, Statistical Analysis of Structural Information (SASI) is introduced as a representation of texture. SASI is based on statistics of clique autocorrelation coefficients, calculated over structuring windows. SASI defines a set of clique windows to extract and measure various structural properties of texture by using a spatial multi-resolution method. Experimental results, performed on various image databases, indicate that SASI is more successful then the Gabor Filter descriptors in capturing small granularities and discontinuities such as sharp corners and abrupt changes. Due to the flexibility in designing the clique windows, SASI reaches higher average retrieval rates compared to Gabor Filter descriptors. However, the price of this performance is increased computational complexity.

This thesis studies the new possibilities to gesture interfaces that emerged with a Leap Motion sensor. The Leap Motion is an innovative, 3D motion capturing device designed especially for hands and fingers tracking with precision up to 0.01mm. The outcome of the thesis is the LeapGesture library dedicated to the developers for Leap Motion Controller that contains algorithms allowing to learn and recognize gestures. The authors examined the data provided by the sensor in context of recognition of hand poses (static gestures), hand movements (dynamic gestures) and in task of a finger recognition. The static gestures are recognized using the Support Vector Machine (SVM) with median filtering an input data and using the correspondences between consecutive recognitions. The thesis contains evaluation of different feature sets, which have a significant impact on the recognition rate. The chosen feature set allowed to recognize a set of five gestures with 99% accuracy and a set of ten gestures with 85%. The dynamic gestures (movements of a hand and fingers) are recognized with the Hidden Markov Models (HMM). Recognition with HMMs allowed to achieve accuracy of 80% for a set containing six classes of dynamic gestures. Finger recognition algorithms proposed in this thesis works with 93% accuracy on a recorded dataset. The LeapGesture library contains presented approaches using a C++ interface, that can be easily used in any application for many purposes.

The conventional audio fingerprinting system by Haitsma uses a lookup table to identify the candidate songs in the database, which contains the sub-fingerprints of songs, and searches the candidates to find a song whose bit error rate is the lowest. However, this approach has a drawback that the number of database accesses increases dramatically, especially when the database contains a large number of songs or when a matching sub-fingerprint is not found in the lookup table due to a heavily degraded input signal. In this paper, a novel search method is proposed to overcome these difficulties. The proposed method partitions each song found from the lookup table into blocks, assigns a weight to each block, and uses the weight as a search priority to speed up the search process while reducing the number of database accesses. Various results from our experiment show the significant improvement in search speed while maintaining the search accuracy comparable to the conventional method.

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

Previous work on muscle activity sensing has leveraged specialized sensors such as electromyography and force sensitive resistors. While these sensors show great potential for detecting finger/hand gestures, they require additional hardware that adds to the cost and user discomfort. Past research has utilized sensors on commercial devices, focusing on recognizing gross hand gestures. In this work we present Serendipity, a new technique for recognizing unremarkable and fine-motor finger gestures using integrated motion sensors (accelerometer and gyroscope) in off-the-shelf smartwatches. Our system demonstrates the potential to distinguish 5 fine-motor gestures like pinching, tapping and rubbing fingers with an average f1-score of 87%. Our work is the first to explore the feasibility of using solely motion sensors on everyday wearable devices to detect fine-grained gestures. This promising technology can be deployed today on current smartwatches and has the potential to be applied to cross-device interactions, or as a tool for research in fields involving finger and hand motion.

This paper presents a novel and efficient facial image representation based on local binary pattern (LBP) texture features. The face image is divided into several regions from which the LBP feature distributions are extracted and concatenated into an enhanced feature vector to be used as a face descriptor. The performance of the proposed method is assessed in the face recognition problem under different challenges. Other applications and several extensions are also discussed

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.

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.


Question: FingerCode: A filterbank technique for fingerprint representation and matching.

This paper delves into the foundational concept of signal detectability, a critical issue in signal processing where an observer is tasked with discerning whether a given voltage signal, observed over a specific interval, originates from noise alone or from a combination of signal and noise. The central query addressed here involves identifying the most effective strategy for an observer to make this determination, as well as pinpointing the specific receiver configuration that embodies this strategy. The paper commences with a detailed theoretical exploration of the subject matter, followed by detailed derivations of the optimal receiver for a range of scenarios of practical significance. The solution to the problem involves the receiver whose output is the likelihood ratio of the input voltage over the observation period. This receiver is universally applicable, regardless of whether one employs the Neyman-Pearson observer, Siegert's ideal observer, or the Woodward and Davies' observer. An optimal observer, tasked with delivering a definitive 'yes' or 'no' response, selects a threshold level, concluding that the input signal is a composite of signal and noise only when this level is surpassed by the output of the likelihood ratio receiver. Each such threshold is associated with conditional probabilities, namely the probability of a false alarm and the probability of detection. Graphical representations of these probabilities, known as receiver operating characteristic (ROC) curves, are instrumental in receiver evaluation. Altering the detection scenario, for instance by modifying signal power, results in a series of ROC curves. Derivations of explicit formulas for the likelihood ratio, probability of detection, and probability of false alarm are provided for a variety of specific cases under the assumption of stationary, band-limited, white Gaussian noise. The selection of seven representative cases was guided by their simplicity and relevance to practical situations. Two of these cases provide the foundation for the best approximation to the challenge of determining detection probability under conditions of uncertainty regarding signal onset time or frequency, or both. These cases also facilitate the exploration of the quantitative relationship between signal uncertainty and detection capability. The diverse array of examples offered herein should inspire methodologies for addressing other straightforward signal detection challenges and provide insights into more complex problems that resist direct solution.

This paper deals with the extraction of a new low level feature that combines, in one histogram, color and texture information. This feature is named FCTH - Fuzzy Color and Texture Histogram - and results from the combination of 3 fuzzy systems. FCTH size is limited to 72 bytes per image, rendering this descriptor suitable for use in large image databases. The proposed feature is appropriate for accurately retrieving images even in distortion cases such as deformations, noise and smoothing. It is tested on a large number of images selected from proprietary image databases or randomly retrieved from popular search engines. To evaluate the performance of the proposed feature, the averaged normalized modified retrieval rank was used. An online demo that implements the proposed feature in an image retrieval system is available at: http://orpheus.ee.duth.gr/image_retrieval.

The electrocardiogram (ECG) is an emerging biometric modality that has seen about 13 years of development in peer-reviewed literature, and as such deserves a systematic review and discussion of the associated methods and findings. In this paper, we review most of the techniques that have been applied to the use of the electrocardiogram for biometric recognition. In particular, we categorize the methodologies based on the features and the classification schemes. Finally, a comparative analysis of the authentication performance of a few of the ECG biometric systems is presented, using our inhouse database. The comparative study includes the cases where training and testing data come from the same and different sessions (days). The authentication results show that most of the algorithms that have been proposed for ECG-based biometrics perform well when the training and testing data come from the same session. However, when training and testing data come from different sessions, a performance degradation occurs. Multiple training sessions were incorporated to diminish the loss in performance. That notwithstanding, only a few of the proposed ECG recognition algorithms appear to be able to support performance improvement due to multiple training sessions. Only three of these algorithms produced equal error rates (EERs) in the single digits, including an EER of 5.5% using a method proposed by us.

-This, paper introduces a new method for describing signature images. The description involves features of the signatures and relations among its parts organized in a hierarchical structure. The description is used to classify the signatures and globally analyse their structure. Description Segmentation Feature extraction Classification Signature analysis I. I N T R O D U C T I O N Although a lot of research on signatures has been reported in the past 20 years (t) they are mainly confined to verification in which a set of goal oriented features is used to give the decision about the authenticity of the claimed signature. However, it is interesting to be able to access a signature database using a compact and meaningful description derived from its pictorial content. Constructing such a signature database, requires an automatic description of signature images. Researches on signature description are very limited and appeared implicitly in verification researches. In on-line systems, Lamarche and Plamondon (z) reconsidered the theoretical model of the handwriting process introduced by Verdenberg and Koster <3) to describe the pen tip speed and orientation and used this description for feature extraction. In off-line systems, Nagel and Rosenfeld (̀ ~ modified the model introduced by Eden (~) for describing upright handwriting by adding the slant parameter and used the parameters of the modified model as a basis for choosing their features for verification, and Brocklehurst (6) used data reduction techniques to encode the signatures into a list of 7 features (called encoded description) and used them for verification. In all cases, neither a structural description of the signature as a 2-D image nor a conceptualization of the features was proposed. In this paper, we introduce a method for signature description and a reliable approach that can describe digitized signature images by this method. The description uses a hierarchical approach, i.e. the signature is at the top level and each lower level gives more detail about the higher one. Since the range of the natural variation of signature writing will change with time as found by Kapoor <~ and Evett, (s) and the ratios of the writing parameters for different samples of the same individual's writing remain essentially constant over a wide range of the writing surface and space available, ('~ features and relations of o u r description are extracted in a relative way (e.g. the length is measured by its ratio to the width, and so on) to attempt to make it insensitive to the natural variation in the genuine signatures. The signature is described using a global description suitable for purposes like classifying the signature data and studying its nature, and a local description convenient for applications which require more detailed information about the signature like signature analysis or similarity oriented retrieval. The global description is represented by a character string, and the local description is represented by a tree structure. In Section 2, the general approach for describing signature images is introduced, and Section 3 introduces the practical method of the description. Section 4 presents the application of the global description to classifying and analysing the signature data, and finally, Section 5 contains the closing remarks. 2. SIGNATURE DESCRIPTION Signatures are a special case of handwriting in which special characters and flourishes are available, and the ratio between the middle zone width and the letter heights is sometimes abnormal. In many cases, the signature is not readable even by a human. Therefore, we deal with the signature as an image that has the principal zones (upper, middle and lower zones) of the handwriting, not as strokes, letters and words. As indicated by Rosenfeld, (9) description of a picture involves properties of the picture or its parts and relationships among them. Our description involves features like slant, and relations among signature properties like the comparison between the widths of the zones. The description is not reversible, that is, the signature cannot be reconstructed from its description. However, the description involves sufficient information to characterize the signature and its parts. Features and relations are extracted quantitatively, and then mapped into a conceptual

Gesture-based interaction, as a natural way for human-computer interaction, has a wide range of applications in ubiquitous computing environment. This paper presents an acceleration-based gesture recognition approach, called FDSVM (Frame-based Descriptor and multi-class SVM), which needs only a wearable 3-dimensional accelerometer. With FDSVM, firstly, the acceleration data of a gesture is collected and represented by a frame-based descriptor, to extract the discriminative information. Then a SVM-based multi-class gesture classifier is built for recognition in the nonlinear gesture feature space. Extensive experimental results on a data set with 3360 gesture samples of 12 gestures over weeks demonstrate that the proposed FDSVM approach significantly outperforms other four methods: DTW, Naïve Bayes, C4.5 and HMM. In the user-dependent case, FDSVM achieves the recognition rate of 99.38% for the 4 direction gestures and 95.21% for all the 12 gestures. In the user-independent case, it obtains the recognition rate of 98.93% for 4 gestures and 89.29% for 12 gestures. Compared to other accelerometer-based gesture recognition approaches reported in literature FDSVM gives the best resulrs for both user-dependent and user-independent cases.

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.

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

In this paper, we propose the Perceptual Shape Decomposition (PSD) to detect fingers for a Kinect-based hand gesture recognition system. The PSD is formulated as a discrete optimization problem by removing all negative minima with minimum cost. Experiments show that our PSD is perceptually relevant and robust against distortion and hand variations, and thus improves the recognition system performance. key words: Kinect camerta, hand gesture recognition, perceptual decomposition, finger detection

Recognition of human beings using shapes of their full facial surfaces is a difficult problem. Our approach is to approximate a facial surface using a collection of (closed) facial curves, and to compare surfaces by comparing their corresponding curves. The differences between shapes of curves are quantified using lengths of geodesic paths between them on a pre-defined curve shape space. The metric for comparing facial surfaces is a composition of the metric involving individual facial curves. These ideas are demonstrated in the context of face recognition using the nearest-neighbor classifier

In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.

This paper presents a novel Gabor-based kernel principal component analysis (PCA) method by integrating the Gabor wavelet representation of face images and the kernel PCA method for face recognition. Gabor wavelets first derive desirable facial features characterized by spatial frequency, spatial locality, and orientation selectivity to cope with the variations due to illumination and facial expression changes. The kernel PCA method is then extended to include fractional power polynomial models for enhanced face recognition performance. A fractional power polynomial, however, does not necessarily define a kernel function, as it might not define a positive semidefinite Gram matrix. Note that the sigmoid kernels, one of the three classes of widely used kernel functions (polynomial kernels, Gaussian kernels, and sigmoid kernels), do not actually define a positive semidefinite Gram matrix either. Nevertheless, the sigmoid kernels have been successfully used in practice, such as in building support vector machines. In order to derive real kernel PCA features, we apply only those kernel PCA eigenvectors that are associated with positive eigenvalues. The feasibility of the Gabor-based kernel PCA method with fractional power polynomial models has been successfully tested on both frontal and pose-angled face recognition, using two data sets from the FERET database and the CMU PIE database, respectively. The FERET data set contains 600 frontal face images of 200 subjects, while the PIE data set consists of 680 images across five poses (left and right profiles, left and right half profiles, and frontal view) with two different facial expressions (neutral and smiling) of 68 subjects. The effectiveness of the Gabor-based kernel PCA method with fractional power polynomial models is shown in terms of both absolute performance indices and comparative performance against the PCA method, the kernel PCA method with polynomial kernels, the kernel PCA method with fractional power polynomial models, the Gabor wavelet-based PCA method, and the Gabor wavelet-based kernel PCA method with polynomial kernels.


Question: FingerCode: A filterbank technique for fingerprint representation and matching.

In this article, we explore the utilization of textural features derived from graytone spatial dependencies for the classification of images, applicable across diverse domains such as photomicrographs, aerial photographs, and satellite imagery. These textural features serve as critical descriptors in the identification of objects or regions of interest within images, regardless of whether they are microscopic textures, land use categories from aerial views, or satellite images. The textural characteristics we investigate are computationally straightforward, making them particularly suitable for practical implementation. We apply these features in three distinct image datasets: photomicrographs of various sandstone types, aerial photographs depicting eight different land use categories at a scale of 1:20,000, and multispectral imagery from the Earth Resources Technology Satellite (ERTS) encompassing seven land use categories. For decision-making, we employ two types of rules: one that defines decision regions as convex polyhedra (a piecewise linear decision rule), and another where decision regions are represented by rectangular parallelpipeds (a min-max decision rule). To evaluate the effectiveness of our approach, each dataset is split into training and test sets. The classification accuracy achieved on the test sets is noteworthy, with 89 percent for photomicrographs, 82 percent for aerial photographic imagery, and 83 percent for satellite imagery. These outcomes suggest that the proposed computationally efficient textural features are broadly applicable and effective for a wide spectrum of image classification tasks.

This report examines the feasibility of using biometrics as a means of establishing a unique identity, to support the proposed entitlement scheme under development by the United Kingdom Passport Service (UKPS) and Driver and Vehicle Licensing Agency (DVLA). Biometrics identification systems measure physiological and behavioural characteristics of a person, and use these measurements to reliably distinguish one person from another. The main examples considered in this study are fingerprint, iris and face image recognition. Biometric identification can assist in the issue of entitlement cards, passports, driving licences and other identity documents in two ways. On the initial issue of such documents, biometrics can be used to check that applicants are not erroneously issued documents using two different identity details; this is the main focus of this study. Secondly, when an entitlement card, passport or driving licence is being used, biometrics can help confirm that it is being used by the correct person. The purpose of the study is to assess the feasibility of fingerprint, iris and face recognition technologies for these applications, to identify unknowns and the risks associated with the use of biometrics in such a national identity scheme, and to make recommendations for how some of these risks might be addressed should such a scheme proceed. Biometric methods do not offer 100% certainty of authentication of individuals. The success of any deployment of a system using biometric methods depends, therefore, on many factors such as: the degree of the 'uniqueness' of the biometric measure, technical and social factors, user interfaces, and provision of secure backup systems for those situations and individual s where the biometric will not work effectively. The main findings of the study are: a In principle, fingerprint or iris recognition can provide the identification performance required for unique identification over the entire UK adult population. In the case of fingerprint recognition, the system would require the enrolment of at least four fingers, whereas for iris recognition both irises should be registered. However, the practicalities of deploying either iris or fingerprint recognition in such a scheme are far from straightforward. b Such a system would be a groundbreaking deployment for this kind of biometric application. Not only would it be one of the largest deployments to date, but aspects of its performance would be far more demanding than those of similarly sized systems; such existing systems are either not applied in the civil sector, or operate …

An audio fingerprint is a compact digest derived from perceptually relevant aspects of a recording. Fingerprinting technologies allow the monitoring of audio content without the need of meta-data or watermark embedding. However, additional uses exist for audio fingerprinting and some are reviewed in this article.

Multibiometric systems are being increasingly de- ployed in many large-scale biometric applications (e.g., FBI-IAFIS, UIDAI system in India) because they have several advantages such as lower error rates and larger population coverage compared to unibiometric systems. However, multibiometric systems require storage of multiple biometric templates (e.g., fingerprint, iris, and face) for each user, which results in increased risk to user privacy and system security. One method to protect individual templates is to store only the secure sketch generated from the corresponding template using a biometric cryptosystem. This requires storage of multiple sketches. In this paper, we propose a feature-level fusion framework to simultaneously protect multiple templates of a user as a single secure sketch. Our main contributions include: (1) practical implementation of the proposed feature-level fusion framework using two well-known biometric cryptosystems, namery,fuzzy vault and fuzzy commitment, and (2) detailed analysis of the trade-off between matching accuracy and security in the proposed multibiometric cryptosystems based on two different databases (one real and one virtual multimodal database), each containing the three most popular biometric modalities, namely, fingerprint, iris, and face. Experimental results show that both the multibiometric cryptosystems proposed here have higher security and matching performance compared to their unibiometric counterparts.

Biometric recognition is an integral component of modern identity management and access control systems. Due to the strong and permanent link between individuals and their biometric traits, exposure of enrolled users' biometric information to adversaries can seriously compromise biometric system security and user privacy. Numerous techniques have been proposed for biometric template protection over the last 20 years. While these techniques are theoretically sound, they seldom guarantee the desired noninvertibility, revocability, and nonlinkability properties without significantly degrading the recognition performance. The objective of this work is to analyze the factors contributing to this performance divide and highlight promising research directions to bridge this gap. The design of invariant biometric representations remains a fundamental problem, despite recent attempts to address this issue through feature adaptation schemes. The difficulty in estimating the statistical distribution of biometric features not only hinders the development of better template protection algorithms but also diminishes the ability to quantify the noninvertibility and nonlinkability of existing algorithms. Finally, achieving nonlinkability without the use of external secrets (e.g., passwords) continues to be a challenging proposition. Further research on the above issues is required to cross the chasm between theory and practice in biometric template protection.

Fingerprint features can be divided into three major categories based on the granularity at which they are extracted: level 1, level 2, and level 3 features. Orientation field, ridge frequency field, and minutiae set are three fundamental components of fingerprint, where the orientation field and ridge frequency field are regarded as level 1 features and minutiae set as level 2 features. It is generally believed that level 1 features, especially orientation field, can be reconstructed from level 2 features, i.e., minutiae. However, it is still a question that if minutiae can be extracted from level 1 features. In this paper, we analyze the relations between level 1 and level 2 features using the frequency modulation (FM) model and propose an approach to extract minutiae from level 1 features (i.e., orientation field and frequency field). The proposed algorithm is evaluated on NIST SD27 and FVC2002 DB1 databases. The true detection rate (TDR) and false detection rate (FDR) of minutiae detection on NIST SD27 and FVC2002 DB1 are about 45% and 30% compared with manually marked minutiae, respectively, with level 1 features extracted at a block size of 16 pixels. When pixelwise orientation and frequency fields are available, TDR and FDR can reach 70% and 25%, respectively. With a smaller block size, the minutiae recovering accuracy can be even higher. Our quantitative and experimental results show the deep relationship between level 1 and level 2 features of a fingerprint.

CH2118-8/85/0000-0387 $1.00 © 1985 IEEE 387 ABSTRACT. In this study a vector quantIzation (VQ) codebook was system. In the other, Shore and Burton 112] used word-based VQ used as an efficient means of characterizing the short-time spectral codebooks and reported good performance in speaker-trained isolatedfeatures of a speaker. A set of such codebooks were then used to word recognition experiments. Here, instead of using word-based VQ recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) codebooks to characterize the phonetic contents of isolated words, we propose to use speaker-based VQ codebooks to characterize the classification rule. A series of speaker recognition experiments was variability of short-time acoustic features of speakers. performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification H. Speaker-based VQ Codebook Approach to Speaker accuracy was achieved. The effects, on performance, of different Characterization and Recognition system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions Were also studied in detail. A set of short-time raw feature vectors of a speaker can be used directly to represent the essential acoustical, phonological or physiological characteristics of that speaker if the training set includes sufficient variations. However such a direct representation is not practical when the number of training vectors is large. The memory requirements for storage and computational complexity in the recognition phase eventually become prohibitively high. Therefore an Automatic speaker recognition has long been an interesting and challenging problem to speech researchers [1-101. The problem, efficient way of compressing the training data had to be found. In order to compress the original data to a small set of representative depending upon the nature of the final task, can be classified into two points, we used a VQ codebook with a small number of codebook different categories: speaker verification and speaker identification. In entries. a speaker verification task, the recognizer is asked to verify an identity claim made by an unknown speaker and a decision to reject or accept the identity claim is made. In a speaker identification task the recognizer is asked to decide which out of a population of N speakers is best classified as the unknown speaker. The decision may include a choice of "none of the above" (i.e., a choice that the specific speaker is not in a given closed set of speakers). The input speech material used for speaker recognition can be either text-dependent (text-constrained) or text-independent (text-free). In the text-dependent mode the speaker is asked to utter a prescribed text. The utterance is then The speaker-based VQ codebook generation can be summarized as follows: Given a set of I training feature vectors, {a1,a2 a) characterizing the variability of a speaker, we want to find a partitioning of the feature vector space, {S1,S2 SM}, for that particular speaker where, 5, the whole feature space is represented as S S1 US2 U . . . US. Each partition, S, forms a convex, nonoverlapping region and every vector inside S is represented by the respo5'tis'1g ceatcoid. vector, b1, cf 5,. The p titioning is done in such a way that the average distortion

Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.

Orientation coding based palmprint verification methods, such as competitive code, palmprint orientation code and robust line orientation code, are state-of-the-art verification algorithms with fast matching speeds. Orientation code makes use of two types of distance measure, SUM_XOR (angular distance) and OR_XOR (Hamming distance), yet little is known about the similarities and differences both SUM_XOR and OR_XOR can be regarded as special cases, and provide some principles for determining the parameters of the unified distance. Experimental results show that, using the same feature extraction and coding methods, the unified distance measure gets lower equal error rates than the original distance measures. & 2009 Elsevier B.V. All rights reserved.

This paper presents a novel minutiae matching method that describes elastic distortions in 4ngerprints by means of a thin-plate spline model, which is estimated using a local and a global matching stage. After registration of the 4ngerprints according to the estimated model, the number of matching minutiae can be counted using very tight matching thresholds. For deformed 4ngerprints, the algorithm gives considerably higher matching scores compared to rigid matching algorithms, while only taking 100 ms on a 1 GHz P-III machine. Furthermore, it is shown that the observed deformations are di6erent from those described by theoretical models proposed in the literature. ? 2003 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.


Question: FingerCode: A filterbank technique for fingerprint representation and matching.

This paper outlines a novel neural network architecture designed to compute complete discrete 2-D Gabor transforms for image analysis and compression, addressing the complexities of conjoint spatiospectral representations. These representations offer a comprehensive image description by integrating locally windowed 2-D spectral information within a global 2-D spatial context, enabling compact image codes by extracting inherent image redundancies. However, the computation of these conjoint transforms is challenging due to the non-orthogonality of elementary expansion functions. A previous orthogonality approach by Bastiaans, while applicable to 1-D signals, is limited by constraints on conjoint sampling rates, window function invariance, and the non-local nature of auxiliary orthogonalizing functions. The proposed neural network, comprising two fixed-weight layers and one adjustable-weight layer, overcomes these limitations by efficiently computing coefficients for complete 2-D Gabor transforms. For non-complete transforms, which may indicate specific image features, the network optimizes coefficients for minimal mean-squared-error representation. In a scheme allowing for exact reconstruction, the network reduces entropy from 7.57 in the pixel format to 2.55 in the complete 2-D Gabor transform. Image compression, achieved through biologically inspired log-polar ensembles of dilations, rotations, and translations of a single 2-D Gabor wavelet, demonstrates ratios up to 20:1. Additionally, image segmentation is performed by clustering coefficients in the complete 2-D Gabor transform. The network's neuroscientific relevance is underscored by its use of empirical 2-D receptive field profiles, derived from orientation-selective neurons in cat visual cortex, to mimic biological visual strategies for embedding angular and spectral analysis within global spatial coordinates. This innovative approach to non-orthogonal image transforms holds potential for both theoretical and practical advancements in image processing and analysis.

This paper describes a patch-based approach for rapid image correlation or template matching. By representing a template image with an ensemble of patches, the method is robust with respect to variations such as local appearance variation, partial occlusion, and scale changes. Rectangle filters are applied to each image patch for fast filtering based on the integral image representation. A new method is developed for feature dimension reduction by detecting the "salient" image structures given a single image. Experiments on a variety images show the success of the method in dealing with different variations in the test images. In terms of computation time, the approach is faster than traditional methods by up to two orders of magnitude and is at least three times faster than a fast implementation of normalized cross correlation.

Template matching is a technique widely used for finding patterns in digital images. An efficient template matching algorithm should be able to detect template instances that have undergone geometric transformations. Similarly, a color template matching should be able to deal with color constancy problem. Recently we have proposed a new grayscale template matching algorithm named Ciratefi, invariant to rotation, scale, translation, brightness and contrast. In this paper we introduce the Color-Ciratefi that takes into account the color information. We use a new similarity metric in the CIELAB space to obtain invariance to brightness and contrast changes. Experiments show that Color-Ciratefi is more accurate than C-color-SIFT, the well-known SIFT algorithm that uses a set of color invariants. In conventional computers, Color-Ciratefi is slower than C-color-SIFT. However Color-Ciratefi is more suitable than C-color-SIFT to be implemented in highly parallel architectures like FPGA, because it repeats exactly the same set of operations for each pixel.

Biometric systems are quite common in our everyday life. Despite the higher difficulty to circumvent them, nowadays criminals are developing techniques to accurately simulate physical, physiological, and behavioral traits of valid users, process known as spoofing attack. In this context, robust countermeasure methods must be developed and integrated with the traditional biometric applications in order to prevent such frauds. Despite face being a promising trait due to its convenience and acceptability, face recognition systems can be easily fooled with common printed photographs. Most of state-of-the-art antispoofing techniques for face recognition applications extract handcrafted texture features from images, mainly based on the efficient local binary patterns (LBP) descriptor, to characterize them. However, recent results indicate that high-level (deep) features are more robust for such complex tasks. In this brief, a novel approach for face spoofing detection that extracts deep texture features from images by integrating the LBP descriptor to a modified convolutional neural network is proposed. Experiments on the NUAA spoofing database indicate that such deep neural network (called LBPnet) and an extended version of it (n-LBPnet) outperform other state-of-the-art techniques, presenting great results in terms of attack detection.

The objective of the gesture recognition is to identify and distinguish the human gestures and utilizes these identified gestures for applications in specific domain. In this paper we propose a new approach to build a real time system to identify the standard gesture given by American Sign Language, or ASL, the dominant sign language of Deaf Americans, including deaf communities in the United States, in the English-speaking parts of Canada, and in some regions of Mexico. We propose a new method of improvised scale invariant feature transform (SIFT) and use the same to extract the features. The objective of the paper is to decode a gesture video into the appropriate alphabets.

Local binary patterns LBP, is one of the features which has been used for texture classification. In this paper, a method based on using these features is proposed for detecting defects in patterned fabrics. In the training stage, at first step LBP operator is applied to all rows (columns) of a defect free fabric sample, pixel by pixel, and the reference feature vector is computed. Then this image is divided into windows and LBP operator is applied to each row (column) of these windows. Based on comparison with the reference feature vector a suitable threshold for defect free windows is found. In the detection stage, a test image is divided into windows and using the threshold, defective windows can be detected. The proposed method is simple and gray scale invariant. Because of its simplicity, online implementation is possible as well.

CH2118-8/85/0000-0387 $1.00 © 1985 IEEE 387 ABSTRACT. In this study a vector quantIzation (VQ) codebook was system. In the other, Shore and Burton 112] used word-based VQ used as an efficient means of characterizing the short-time spectral codebooks and reported good performance in speaker-trained isolatedfeatures of a speaker. A set of such codebooks were then used to word recognition experiments. Here, instead of using word-based VQ recognize the identity of an unknown speaker from his/her unlabelled spoken utterances based on a minimum distance (distortion) codebooks to characterize the phonetic contents of isolated words, we propose to use speaker-based VQ codebooks to characterize the classification rule. A series of speaker recognition experiments was variability of short-time acoustic features of speakers. performed using a 100-talker (50 male and 50 female) telephone recording database consisting of isolated digit utterances. For ten random but different isolated digits, over 98% speaker identification H. Speaker-based VQ Codebook Approach to Speaker accuracy was achieved. The effects, on performance, of different Characterization and Recognition system parameters such as codebook sizes, the number of test digits, phonetic richness of the text, and difference in recording sessions Were also studied in detail. A set of short-time raw feature vectors of a speaker can be used directly to represent the essential acoustical, phonological or physiological characteristics of that speaker if the training set includes sufficient variations. However such a direct representation is not practical when the number of training vectors is large. The memory requirements for storage and computational complexity in the recognition phase eventually become prohibitively high. Therefore an Automatic speaker recognition has long been an interesting and challenging problem to speech researchers [1-101. The problem, efficient way of compressing the training data had to be found. In order to compress the original data to a small set of representative depending upon the nature of the final task, can be classified into two points, we used a VQ codebook with a small number of codebook different categories: speaker verification and speaker identification. In entries. a speaker verification task, the recognizer is asked to verify an identity claim made by an unknown speaker and a decision to reject or accept the identity claim is made. In a speaker identification task the recognizer is asked to decide which out of a population of N speakers is best classified as the unknown speaker. The decision may include a choice of "none of the above" (i.e., a choice that the specific speaker is not in a given closed set of speakers). The input speech material used for speaker recognition can be either text-dependent (text-constrained) or text-independent (text-free). In the text-dependent mode the speaker is asked to utter a prescribed text. The utterance is then The speaker-based VQ codebook generation can be summarized as follows: Given a set of I training feature vectors, {a1,a2 a) characterizing the variability of a speaker, we want to find a partitioning of the feature vector space, {S1,S2 SM}, for that particular speaker where, 5, the whole feature space is represented as S S1 US2 U . . . US. Each partition, S, forms a convex, nonoverlapping region and every vector inside S is represented by the respo5'tis'1g ceatcoid. vector, b1, cf 5,. The p titioning is done in such a way that the average distortion

-This paper presents a texture segmentation algorithm inspired by the multi-channel filtering theory for visual information processing in the early stages of human visual system. The channels are characterized by a bank of Gabor filters that nearly uniformly covers the spatial-frequency domain, and a systematic filter selection scheme is proposed, which is based on reconstruction of the input image from the filtered images. Texture features are obtained by subjecting each (selected) filtered image to a nonlinear transformation and computing a measure of "energy" in a window around each pixel. A square-error clustering algorithm is then used to integrate the feature images and produce a segmentation. A simple procedure to incorporate spatial information in the clustering process is proposed. A relative index is used to estimate the "'true" number of texture categories. Texture segmentation Multi-channel filtering Clustering Clustering index Gabor filters Wavelet transform I . I N T R O D U C T I O N Image segmentation is a difficult yet very important task in many image analysis or computer vision applications. Differences in the mean gray level or in color in small neighborhoods alone are not always sufficient for image segmentation. Rather, one has to rely on differences in the spatial arrangement of gray values of neighboring pixels-that is, on differences in texture. The problem of segmenting an image based on textural cues is referred to as the texture segmentation problem. Texture segmentation involves identifying regions with "uniform" textures in a given image. Appropriate measures of texture are needed in order to decide whether a given region has uniform texture. Sklansky (o has suggested the following definition of texture which is appropriate in the segmentation context: "A region in an image has a constant texture if a set of local statistics or other local properties of the picture are constant, slowly varying, or approximately periodic". Texture, therefore, has both local and global connotations--i t is characterized by invariance of certain local measures or properties over an image region. The diversity of natural and artificial textures makes it impossible to give a universal definition of texture. A large number of techniques for analyzing image texture has been proposed in the past two decades/2,3) In this paper, we focus on a particular approach to texture analysis which is referred to as ° This work was supported in part by the National Science Foundation infrastructure grant CDA-8806599, and by a grant from E. I. Du Pont De Nemours & Company Inc. the multi-channel filtering approach. This approach is inspired by a multi-channel filtering theory for processing visual information in the early stages of the human visual system. First proposed by Campbell and Robson (4) the theory holds that the visual system decomposes the retinal image into a number of filtered images, each of which contains intensity variations over a narrow range of frequency (size) and orientation. The psychophysical experiments that suggested such a decomposition used various grating patterns as stimuli and were based on adaptation techniques. I~l Subsequent psychophysiological experiments provided additional evidence supporting the theory. De Valois et al. ,(5) for example, recorded the response of simple cells in the visual cortex of the Macaque monkey to sinusoidal gratings with different frequencies and orientations. It was observed that each cell responds to a narrow range of frequency and orientation only. Therefore, it appears that there are mechanisms in the visual cortex of mammals that are tuned to combinations of frequency and orientation in a narrow range. These mechanisms are often referred to as channels, and are appropriately interpreted as band-pass filters. The multi-channel filtering approach to texture analysis is intuitively appealing because it allows us to exploit differences in dominant sizes and orientations of different textures. Today, the need for a multi-resolution approach to texture analysis is well recognized. While other approaches to texture analysis have had to be extended to accommodate this paradigm, the multi-channel filtering approach, is inherently multi-resolutional. Another important

We present randomized algorithms to solve the following string-matching problem and some of its generalizations: Given a string X of length n (the pattern) and a string Y (the text), find the first occurrence of X as a consecutive block within Y. The algorithms represent strings of length n by much shorter strings called fingerprints, and achieve their efficiency by manipulating fingerprints instead of longer strings. The algorithms require a constant number of storage locations, and essentially run in real time. They are conceptually simple and easy to implement. The method readily generalizes to higher-dimensional patternmatching problems.

The accurate classification of static hand gestures is a vital role to develop a hand gesture recognition system which is used for human-computer interaction (HCI) and for human alternative and augmentative communication (HAAC) application. A vision-based static hand gesture recognition algorithm consists of three stages: preprocessing, feature extraction and classification. The preprocessing stage involves following three sub-stages: segmentation which segments hand region from its background images using a histogram based thresholding algorithm and transforms into binary silhouette; rotation that rotates segmented gesture to make the algorithm, rotation invariant; filtering that effectively removes background noise and object noise from binary image by morphological filtering technique. To obtain a rotation invariant gesture image, a novel technique is proposed in this paper by coinciding the 1st principal component of the segmented hand gestures with vertical axes. A localized contour sequence (LCS) based feature is used here to classify the hand gestures. A k-mean based radial basis function neural network (RBFNN) is also proposed here for classification of hand gestures from LCS based feature set. The experiment is conducted on 500 train images and 500 test images of 25 class grayscale static hand gesture image dataset of Danish/international sign language hand alphabet. The proposed method performs with 99.6% classification accuracy which is better than earlier reported technique.

This paper presents a precise system for handwritten Chinese and Japanese character recognition. Before extracting directional element feature (DEF) from each character image, transformation based on partial inclination detection (TPID) is used to reduce undesired effects of degraded images. In the recognition process, city block distance with deviation (CBDD) and asymmetric Mahalanobis distance (AMD) are proposed for rough classification and fine classification. With this recognition system, the experimental result of the database ETL9B reaches to 99.42%.


Question: SAD: A web session anomaly detection method using parameter estimation.

This paper introduces a specialized anomaly detection technique designed to enhance network intrusion detection capabilities. Given the persistent rise in cyber-attacks, such as the notorious CodeRed worm, there is a pressing need to safeguard network resources effectively. While firewalls serve as the initial barrier against intrusion, intrusion detection systems (IDS) provide a secondary layer of defense by identifying malicious activities post-perimeter breach. IDS solutions can operate on either predefined signatures (signature-based) or a model of normal behavior (anomaly-based). Anomaly-based systems, though capable of identifying novel attacks, face challenges in accurately defining acceptable behavior and managing false alarms triggered by legitimate but unusual activities. To address these limitations, we propose an approach that integrates application-specific knowledge into network services protection. This detailed information allows for the expansion of rudimentary network traffic models into a comprehensive application model, enabling the detection of malicious content embedded within individual packets. We detail the distinctive features of our proposed model and furnish experimental evidence to substantiate the efficacy of our system in enhancing network security.

We present the first large-scale studies of three advanced web tracking mechanisms - canvas fingerprinting, evercookies and use of "cookie syncing" in conjunction with evercookies. Canvas fingerprinting, a recently developed form of browser fingerprinting, has not previously been reported in the wild; our results show that over 5% of the top 100,000 websites employ it. We then present the first automated study of evercookies and respawning and the discovery of a new evercookie vector, IndexedDB. Turning to cookie syncing, we present novel techniques for detection and analysing ID flows and we quantify the amplification of privacy-intrusive tracking practices due to cookie syncing.
 Our evaluation of the defensive techniques used by privacy-aware users finds that there exist subtle pitfalls --- such as failing to clear state on multiple browsers at once - in which a single lapse in judgement can shatter privacy defenses. This suggests that even sophisticated users face great difficulties in evading tracking techniques.

1 Qatar Computing Research Institute (QCRI), Qatar Foundation, Doha, Qatar; E-Mail: ikhalil@uaeu.ac.ae 2 Department of Electrical and Computer Engineering, Newark College of Engineering, New Jersey Institute of Technology, University Heights, Newark, NJ 07102, USA; E-Mail: abdallah@njit.edu 3 College of Information Technology, United Arab Emirates University, PO Box 15551, Al Ain, United Arab Emirates; E-Mail: azeemsamma@hotmail.com

Security and performance are critical goals for distributed systems. The increased design complexity, incomplete expertise of developers, and limited functionality of existing testing tools often result in bugs and vulnerabilities that prevent implementations from achieving their design goals in practice. Many of these bugs, vulnerabilities, and misconfigurations manifest after the code has already been deployed making the debugging process difficult and costly. In this paper, we present Turret, a platform for automatically finding performance attacks in unmodified implementations of distributed systems. Turret does not require the user to provide any information about vulnerabilities and runs the implementation in the same operating system setup as the deployment, with an emulated network. Turret uses a new attack finding algorithm and several optimizations that allow it to find attacks in a matter of minutes. We ran Turret on 5 different distributed system implementations specifically designed to tolerate insider attacks, and found 30 performance attacks, 24 of which were not previously reported to the best of our knowledge.

Since last decades, online technologies have revolutionized the modern computing world. However, as a result, security threats are increasing rapidly. A huge community is using the online services even from chatting to banking is done via online transactions. Customers of web technologies face various security threats and phishing is one of the most important threat that needs to be address. Therefore, the security mechanism must be enhanced. The attacker uses phishing attack to get victims credential information like bank account number, passwords or any other information by mimicking a website of an enterprise, and the victim is unaware of phishing website. In literature, several approaches have been proposed for detection and filtering phishing attack. However, researchers are still searching for such a solution that can provide better results to secure users from phishing attack. Phishing websites have certain characteristics and patterns and to identify those features can help us to detect phishing. To identify such features is a classification task and can be solved using data mining techniques. In this paper, we are presenting a hybrid model for classification to overcome phishing-sites problem. To evaluate this model, we have used the dataset from UCI repository which contains 30 attributes and 11055 instances. The experimental results showed that our proposed hybrid model outperforms in terms of high accuracy and less error rate.

Applicationservicesfor theend-userareall importantin today’scommunicationnetworks,andcoulddictatethesuccessor failureof technologyor serviceproviders[39]. It is importantto develop and deploy applicationfunctionality quickly [18]. The ability to compose servicesfrom independentprovidersprovidesa flexible way to quickly build new end-to-endfunctionality. Suchcompositionof services acrossthe network and acrossdifferent serviceproviders becomesespeciallyimportant in the context of growing popularityandheterogeneityin 3G+ accessnetworks and devices[25]. In this work, we provide a framework for constructing such composed services on the Internet. Robustnessandhigh-availability arecrucial for Internet services.While cluster-basedmodelsfor resilienceto failureshave beenbuilt for web-servers[27] aswell asproxy services[11, 4], theseareinadequatein thecontext of composedservices.This is especiallyso whenthe application sessionis long-lived, andfailureshaveto behandledduring asession. In the context of composed services, we address the important and challenging issues of resilience to failures, and adapting to changes in overall performance during longlived sessions. Our framework is basedon a connection-orientedoverlay network of compute-clusterson theInternet.Theoverlay network provides the context for composingservices over the wide-area,and monitoring for livenessand performanceof a session. We have performedinitial analysesof the feasibility of network failure detectionover the wide-areaInternet. And we have a preliminaryevaluation of the overheadassociatedwith suchan overlay network. We presentour plansfor furtherevaluationandrefinement of thearchitecture;andfor examiningissuesrelatedto the creationof theoverlaytopology. 1 Intr oduction Application functionality for the end-useris thedriving force behinddevelopmentof technologyandits adoption. It is importantto be ableto developanddeploy new functionality quickly [18]. Compositionof existing servicesto achieve new functionalityenablessuchquick development throughreuseof alreadydeployed components.Consider for example,thescenarioshown in Figure1. Video-on-demand server Replicated instances Service Provider A

Web spoofing is a significant problem involving fraudulent email and web sites that trick unsuspecting users into revealing private information. We discuss some aspects of common attacks and propose a framework for client-side defense: a browser plug-in that examines web pages and warns the user when requests for data may be part of a spoof attack. While the plugin, SpoofGuard, has been tested using actual sites obtained through government agencies concerned about the problem, we expect that web spoofing and other forms of identity theft will be continuing problems in

Anomaly detection has always been a critical and challenging problem in many application areas such as industry, healthcare, environment and finance. This problem becomes more di cult in the Big Data era as the data scale increases dramatically and the type of anomalies gets more complicated. In time sensitive applications like real time monitoring, data are often fed in streams and anomalies are required to be identified online across multiple streams with a short time delay. The new data characteristics and analysis requirements make existing solutions no longer suitable. In this paper, we propose a framework to discover a new type of anomaly called contextual collective anomaly over a collection of data streams in real time. A primary advantage of this solution is that it can be seamlessly integrated with real time monitoring systems to timely and accurately identify the anomalies. Also, the proposed framework is designed in a way with a low computational intensity, and is able to handle large scale data streams. To demonstrate the e↵ectiveness and e ciency of our framework, we empirically validate it on two real world applications.

NIMISHA PHILIP, SHERLY K.K Department of Computer Science & Engineering, Toc H Institute of Science & Technology email:nimishavellapally@gmail.com Department of Information Technology, Toc H Institute of Science & Technology, Ernakulam, Kerala, India shrly_shilu@yahoo.com Abstract — Globalization and increased use of the Internet for online shopping has resulted in a considerable proliferation of credit card transactions throughout the world.Higher acceptability and convenience of credit cards for purchases has not only given personal comfort to customers but also attracted a large number of attackers. As a result, credit card payment systems must be supported by efficient fraud detection capability for minimizing unwanted activities by adversaries. Most of the well known algorithms for fraud detection are based on supervised training Every cardholder has a certain shopping behavior, which establishes an activity profile for him. Existing FDS try to capture behavioral patterns as rules which are static .This becomes ineffective when cardholder develops new patterns of behavior Here, we propose a unsupervised method to dynamically profile behavior pattern of customer Then the incoming transactions are compared against the user profile to indicate the anomalies, based on which the corresponding warnings are outputted. A FP tree based pattern matching algorithm is used to evaluate how unusual the new transactions are.

Timing attacks are usually used to attack weak computing devices such as smartcards. We show that timing attacks apply to general software systems. Specifically, we devise a timing attack against OpenSSL. Our experiments show that we can extract private keys from an OpenSSL-based web server running on a machine in the local network. Our results demonstrate that timing attacks against network servers are practical and therefore security systems should defend against them.

This paper proposes a novel anomaly detection system for spacecrafts based on data mining techniques. It constructs a nonlinear probabilistic model w.r.t. behavior of a spacecraft by applying the relevance vector regression and autoregression to massive telemetry data, and then monitors the on-line telemetry data using the model and detects anomalies. A major advantage over conventional anomaly detection methods is that this approach requires little a priori knowledge on the system.