Showing papers on "Facial recognition system published in 2007"

Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions

Abstract: Dynamic texture (DT) is an extension of texture to the temporal domain. Description and recognition of DTs have attracted growing attention. In this paper, a novel approach for recognizing DTs is proposed and its simplifications and extensions to facial image analysis are also considered. First, the textures are modeled with volume local binary patterns (VLBP), which are an extension of the LBP operator widely used in ordinary texture analysis, combining motion and appearance. To make the approach computationally simple and easy to extend, only the co-occurrences of the local binary patterns on three orthogonal planes (LBP-TOP) are then considered. A block-based method is also proposed to deal with specific dynamic events such as facial expressions in which local information and its spatial locations should also be taken into account. In experiments with two DT databases, DynTex and Massachusetts Institute of Technology (MIT), both the VLBP and LBP-TOP clearly outperformed the earlier approaches. The proposed block-based method was evaluated with the Cohn-Kanade facial expression database with excellent results. The advantages of our approach include local processing, robustness to monotonic gray-scale changes, and simple computation

Gesture Recognition: A Survey

Abstract: Gesture recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human-computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation to virtual reality. In this paper, we provide a survey on gesture recognition with particular emphasis on hand gestures and facial expressions. Applications involving hidden Markov models, particle filtering and condensation, finite-state machines, optical flow, skin color, and connectionist models are discussed in detail. Existing challenges and future research possibilities are also highlighted

General Tensor Discriminant Analysis and Gabor Features for Gait Recognition

Abstract: Traditional image representations are not suited to conventional classification methods such as the linear discriminant analysis (LDA) because of the undersample problem (USP): the dimensionality of the feature space is much higher than the number of training samples. Motivated by the successes of the two-dimensional LDA (2DLDA) for face recognition, we develop a general tensor discriminant analysis (GTDA) as a preprocessing step for LDA. The benefits of GTDA, compared with existing preprocessing methods such as the principal components analysis (PCA) and 2DLDA, include the following: 1) the USP is reduced in subsequent classification by, for example, LDA, 2) the discriminative information in the training tensors is preserved, and 3) GTDA provides stable recognition rates because the alternating projection optimization algorithm to obtain a solution of GTDA converges, whereas that of 2DLDA does not. We use human gait recognition to validate the proposed GTDA. The averaged gait images are utilized for gait representation. Given the popularity of Gabor-function-based image decompositions for image understanding and object recognition, we develop three different Gabor-function-based image representations: 1) GaborD is the sum of Gabor filter responses over directions, 2) GaborS is the sum of Gabor filter responses over scales, and 3) GaborSD is the sum of Gabor filter responses over scales and directions. The GaborD, GaborS, and GaborSD representations are applied to the problem of recognizing people from their averaged gait images. A large number of experiments were carried out to evaluate the effectiveness (recognition rate) of gait recognition based on first obtaining a Gabor, GaborD, GaborS, or GaborSD image representation, then using GDTA to extract features and, finally, using LDA for classification. The proposed methods achieved good performance for gait recognition based on image sequences from the University of South Florida (USF) HumanID Database. Experimental comparisons are made with nine state-of-the-art classification methods in gait recognition.

Probabilistic Linear Discriminant Analysis for Inferences About Identity

Abstract: Many current face recognition algorithms perform badly when the lighting or pose of the probe and gallery images differ. In this paper we present a novel algorithm designed for these conditions. We describe face data as resulting from a generative model which incorporates both within-individual and between-individual variation. In recognition we calculate the likelihood that the differences between face images are entirely due to within-individual variability. We extend this to the non-linear case where an arbitrary face manifold can be described and noise is position-dependent. We also develop a "tied" version of the algorithm that allows explicit comparison across quite different viewing conditions. We demonstrate that our model produces state of the art results for (i) frontal face recognition (ii) face recognition under varying pose.

Automatic Age Estimation Based on Facial Aging Patterns

Abstract: While recognition of most facial variations, such as identity, expression, and gender, has been extensively studied, automatic age estimation has rarely been explored. In contrast to other facial variations, aging variation presents several unique characteristics which make age estimation a challenging task. This paper proposes an automatic age estimation method named AGES (AGing pattErn Subspace). The basic idea is to model the aging pattern, which is defined as the sequence of a particular individual's face images sorted in time order, by constructing a representative subspace. The proper aging pattern for a previously unseen face image is determined by the projection in the subspace that can reconstruct the face image with minimum reconstruction error, while the position of the face image in that aging pattern will then indicate its age. In the experiments, AGES and its variants are compared with the limited existing age estimation methods (WAS and AAS) and some well-established classification methods (kNN, BP, C4.5, and SVM). Moreover, a comparison with human perception ability on age is conducted. It is interesting to note that the performance of AGES is not only significantly better than that of all the other algorithms, but also comparable to that of the human observers.

Facial Expression Recognition in Image Sequences Using Geometric Deformation Features and Support Vector Machines

Abstract: In this paper, two novel methods for facial expression recognition in facial image sequences are presented. The user has to manually place some of Candide grid nodes to face landmarks depicted at the first frame of the image sequence under examination. The grid-tracking and deformation system used, based on deformable models, tracks the grid in consecutive video frames over time, as the facial expression evolves, until the frame that corresponds to the greatest facial expression intensity. The geometrical displacement of certain selected Candide nodes, defined as the difference of the node coordinates between the first and the greatest facial expression intensity frame, is used as an input to a novel multiclass Support Vector Machine (SVM) system of classifiers that are used to recognize either the six basic facial expressions or a set of chosen Facial Action Units (FAUs). The results on the Cohn-Kanade database show a recognition accuracy of 99.7% for facial expression recognition using the proposed multiclass SVMs and 95.1% for facial expression recognition based on FAU detection

Learning multi-scale block local binary patterns for face recognition

Abstract: In this paper, we propose a novel representation, calledMultiscale Block Local Binary Pattern (MB-LBP), and apply it to face recognition. The Local Binary Pattern (LBP) has been proved to be effective for image representation, but it is too local to be robust. In MB-LBP, the computation is done based on average values of block subregions, instead of individual pixels. In this way, MB-LBP code presents several advantages: (1) It ismore robust than LBP; (2) it encodes not only microstructures but also macrostructures of image patterns, and hence provides a more complete image representation than the basic LBP operator; and (3) MB-LBP can be computed very efficiently using integral images. Furthermore, in order to reflect the uniform appearance of MB-LBP, we redefine the uniform patterns via statistical analysis. Finally, AdaBoost learning is applied to select most effective uniform MB-LBP features and construct face classifiers. Experiments on Face Recognition Grand Challenge (FRGC) ver2.0 database show that the proposed MB-LBP method significantly outperforms other LBP based face recognition algorithms.

Discriminative Learning and Recognition of Image Set Classes Using Canonical Correlations

Abstract: We address the problem of comparing sets of images for object recognition, where the sets may represent variations in an object's appearance due to changing camera pose and lighting conditions. canonical correlations (also known as principal or canonical angles), which can be thought of as the angles between two d-dimensional subspaces, have recently attracted attention for image set matching. Canonical correlations offer many benefits in accuracy, efficiency, and robustness compared to the two main classical methods: parametric distribution-based and nonparametric sample-based matching of sets. Here, this is first demonstrated experimentally for reasonably sized data sets using existing methods exploiting canonical correlations. Motivated by their proven effectiveness, a novel discriminative learning method over sets is proposed for set classification. Specifically, inspired by classical linear discriminant analysis (LDA), we develop a linear discriminant function that maximizes the canonical correlations of within-class sets and minimizes the canonical correlations of between-class sets. Image sets transformed by the discriminant function are then compared by the canonical correlations. Classical orthogonal subspace method (OSM) is also investigated for the similar purpose and compared with the proposed method. The proposed method is evaluated on various object recognition problems using face image sets with arbitrary motion captured under different illuminations and image sets of 500 general objects taken at different views. The method is also applied to object category recognition using ETH-80 database. The proposed method is shown to outperform the state-of-the-art methods in terms of accuracy and efficiency

Histogram of Gabor Phase Patterns (HGPP): A Novel Object Representation Approach for Face Recognition

Abstract: A novel object descriptor, histogram of Gabor phase pattern (HGPP), is proposed for robust face recognition. In HGPP, the quadrant-bit codes are first extracted from faces based on the Gabor transformation. Global Gabor phase pattern (GGPP) and local Gabor phase pattern (LGPP) are then proposed to encode the phase variations. GGPP captures the variations derived from the orientation changing of Gabor wavelet at a given scale (frequency), while LGPP encodes the local neighborhood variations by using a novel local XOR pattern (LXP) operator. They are both divided into the nonoverlapping rectangular regions, from which spatial histograms are extracted and concatenated into an extended histogram feature to represent the original image. Finally, the recognition is performed by using the nearest-neighbor classifier with histogram intersection as the similarity measurement. The features of HGPP lie in two aspects: 1) HGPP can describe the general face images robustly without the training procedure; 2) HGPP encodes the Gabor phase information, while most previous face recognition methods exploit the Gabor magnitude information. In addition, Fisher separation criterion is further used to improve the performance of HGPP by weighing the subregions of the image according to their discriminative powers. The proposed methods are successfully applied to face recognition, and the experiment results on the large-scale FERET and CAS-PEAL databases show that the proposed algorithms significantly outperform other well-known systems in terms of recognition rate

Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera

Abstract: We present a real-time liveness detection approach against photograph spoofing in face recognition, by recognizing spontaneous eyeblinks, which is a non-intrusive manner. The approach requires no extra hardware except for a generic webcamera. Eyeblink sequences often have a complex underlying structure. We formulate blink detection as inference in an undirected conditional graphical framework, and are able to learn a compact and efficient observation and transition potentials from data. For purpose of quick and accurate recognition of the blink behavior, eye closity, an easily-computed discriminative measure derived from the adaptive boosting algorithm, is developed, and then smoothly embedded into the conditional model. An extensive set of experiments are presented to show effectiveness of our approach and how it outperforms the cascaded Adaboost and HMM in task of eyeblink detection.

Illumination Invariant Face Recognition Using Near-Infrared Images

Abstract: Most current face recognition systems are designed for indoor, cooperative-user applications. However, even in thus-constrained applications, most existing systems, academic and commercial, are compromised in accuracy by changes in environmental illumination. In this paper, we present a novel solution for illumination invariant face recognition for indoor, cooperative-user applications. First, we present an active near infrared (NIR) imaging system that is able to produce face images of good condition regardless of visible lights in the environment. Second, we show that the resulting face images encode intrinsic information of the face, subject only to a monotonic transform in the gray tone; based on this, we use local binary pattern (LBP) features to compensate for the monotonic transform, thus deriving an illumination invariant face representation. Then, we present methods for face recognition using NIR images; statistical learning algorithms are used to extract most discriminative features from a large pool of invariant LBP features and construct a highly accurate face matching engine. Finally, we present a system that is able to achieve accurate and fast face recognition in practice, in which a method is provided to deal with specular reflections of active NIR lights on eyeglasses, a critical issue in active NIR image-based face recognition. Extensive, comparative results are provided to evaluate the imaging hardware, the face and eye detection algorithms, and the face recognition algorithms and systems, with respect to various factors, including illumination, eyeglasses, time lapse, and ethnic groups

Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach

Abstract: In this paper, we present the computational tools and a hardware prototype for 3D face recognition. Full automation is provided through the use of advanced multistage alignment algorithms, resilience to facial expressions by employing a deformable model framework, and invariance to 3D capture devices through suitable preprocessing steps. In addition, scalability in both time and space is achieved by converting 3D facial scans into compact metadata. We present our results on the largest known, and now publicly available, face recognition grand challenge 3D facial database consisting of several thousand scans. To the best of our knowledge, this is the highest performance reported on the FRGC v2 database for the 3D modality

An Efficient Multimodal 2D-3D Hybrid Approach to Automatic Face Recognition

Abstract: We present a fully automatic face recognition algorithm and demonstrate its performance on the FRGC v2.0 data. Our algorithm is multimodal (2D and 3D) and performs hybrid (feature based and holistic) matching in order to achieve efficiency and robustness to facial expressions. The pose of a 3D face along with its texture is automatically corrected using a novel approach based on a single automatically detected point and the Hotelling transform. A novel 3D spherical face representation (SFR) is used in conjunction with the scale-invariant feature transform (SIFT) descriptor to form a rejection classifier, which quickly eliminates a large number of candidate faces at an early stage for efficient recognition in case of large galleries. The remaining faces are then verified using a novel region-based matching approach, which is robust to facial expressions. This approach automatically segments the eyes- forehead and the nose regions, which are relatively less sensitive to expressions and matches them separately using a modified iterative closest point (ICP) algorithm. The results of all the matching engines are fused at the metric level to achieve higher accuracy. We use the FRGC benchmark to compare our results to other algorithms that used the same database. Our multimodal hybrid algorithm performed better than others by achieving 99.74 percent and 98.31 percent verification rates at a 0.001 false acceptance rate (FAR) and identification rates of 99.02 percent and 95.37 percent for probes with a neutral and a nonneutral expression, respectively.

Face Hallucination: Theory and Practice

Abstract: In this paper, we study face hallucination, or synthesizing a high-resolution face image from an input low-resolution image, with the help of a large collection of other high-resolution face images. Our theoretical contribution is a two-step statistical modeling approach that integrates both a global parametric model and a local nonparametric model. At the first step, we derive a global linear model to learn the relationship between the high-resolution face images and their smoothed and down-sampled lower resolution ones. At the second step, we model the residue between an original high-resolution image and the reconstructed high-resolution image after applying the learned linear model by a patch-based non-parametric Markov network to capture the high-frequency content. By integrating both global and local models, we can generate photorealistic face images. A practical contribution is a robust warping algorithm to align the low-resolution face images to obtain good hallucination results. The effectiveness of our approach is demonstrated by extensive experiments generating high-quality hallucinated face images from low-resolution input with no manual alignment.

Enhanced local texture feature sets for face recognition under difficult lighting conditions

Abstract: Recognition in uncontrolled situations is one of the most important bottlenecks for practical face recognition systems. We address this by combining the strengths of robust illumination normalization, local texture based face representations and distance transform based matching metrics. Specifically, we make three main contributions: (i) we present a simple and efficient preprocessing chain that eliminates most of the effects of changing illumination while still preserving the essential appearance details that are needed for recognition; (ii) we introduce Local Ternary Patterns (LTP), a generalization of the Local Binary Pattern (LBP) local texture descriptor that is more discriminant and less sensitive to noise in uniform regions; and (iii) we show that replacing local histogramming with a local distance transform based similarity metric further improves the performance of LBP/LTP based face recognition. The resulting method gives state-of-the-art performance on three popular datasets chosen to test recognition under difficult illumination conditions: Face Recognition Grand Challenge version 1 experiment 4, Extended Yale-B, and CMU PIE.

High-Performance Rotation Invariant Multiview Face Detection

Abstract: Rotation invariant multiview face detection (MVFD) aims to detect faces with arbitrary rotation-in-plane (RIP) and rotation-off-plane (ROP) angles in still images or video sequences. MVFD is crucial as the first step in automatic face processing for general applications since face images are seldom upright and frontal unless they are taken cooperatively. In this paper, we propose a series of innovative methods to construct a high-performance rotation invariant multiview face detector, including the width-first-search (WFS) tree detector structure, the vector boosting algorithm for learning vector-output strong classifiers, the domain-partition-based weak learning method, the sparse feature in granular space, and the heuristic search for sparse feature selection. As a result of that, our multiview face detector achieves low computational complexity, broad detection scope, and high detection accuracy on both standard testing sets and real-life images

Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships

Abstract: A system that could automatically analyze the facial actions in real time has applications in a wide range of different fields. However, developing such a system is always challenging due to the richness, ambiguity, and dynamic nature of facial actions. Although a number of research groups attempt to recognize facial action units (AUs) by improving either the facial feature extraction techniques or the AU classification techniques, these methods often recognize AUs or certain AU combinations individually and statically, ignoring the semantic relationships among AUs and the dynamics of AUs. Hence, these approaches cannot always recognize AUs reliably, robustly, and consistently. In this paper, we propose a novel approach that systematically accounts for the relationships among AUs and their temporal evolutions for AU recognition. Specifically, we use a dynamic Bayesian network (DBN) to model the relationships among different AUs. The DBN provides a coherent and unified hierarchical probabilistic framework to represent probabilistic relationships among various AUs and to account for the temporal changes in facial action development. Within our system, robust computer vision techniques are used to obtain AU measurements. Such AU measurements are then applied as evidence to the DBN for inferring various AUs. The experiments show that the integration of AU relationships and AU dynamics with AU measurements yields significant improvement of AU recognition, especially for spontaneous facial expressions and under more realistic environment including illumination variation, face pose variation, and occlusion.

Spectral Regression for Efficient Regularized Subspace Learning

Abstract: Subspace learning based face recognition methods have attracted considerable interests in recent years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and marginal Fisher analysis (MFA). However, a disadvantage of all these approaches is that their computations involve eigen- decomposition of dense matrices which is expensive in both time and memory. In this paper, we propose a novel dimensionality reduction framework, called spectral regression (SR), for efficient regularized subspace learning. SR casts the problem of learning the projective functions into a regression framework, which avoids eigen-decomposition of dense matrices. Also, with the regression based framework, different kinds of regularizes can be naturally incorporated into our algorithm which makes it more flexible. Computational analysis shows that SR has only linear-time complexity which is a huge speed up comparing to the cubic-time complexity of the ordinary approaches. Experimental results on face recognition demonstrate the effectiveness and efficiency of our method.

Biometric Recognition Using 3D Ear Shape

Abstract: Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.

Learning a Spatially Smooth Subspace for Face Recognition

Abstract: Subspace learning based face recognition methods have attracted considerable interests in recently years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE), marginal fisher analysis (MFA) and local discriminant embedding (LDE). These methods consider an n1timesn2 image as a vector in Rn 1 timesn 2 and the pixels of each image are considered as independent. While an image represented in the plane is intrinsically a matrix. The pixels spatially close to each other may be correlated. Even though we have n1xn2 pixels per image, this spatial correlation suggests the real number of freedom is far less. In this paper, we introduce a regularized subspace learning model using a Laplacian penalty to constrain the coefficients to be spatially smooth. All these existing subspace learning algorithms can fit into this model and produce a spatially smooth subspace which is better for image representation than their original version. Recognition, clustering and retrieval can be then performed in the image subspace. Experimental results on face recognition demonstrate the effectiveness of our method.

Unsupervised Joint Alignment of Complex Images

Abstract: Many recognition algorithms depend on careful positioning of an object into a canonical pose, so the position of features relative to a fixed coordinate system can be examined. Currently, this positioning is done either manually or by training a class-specialized learning algorithm with samples of the class that have been hand-labeled with parts or poses. In this paper, we describe a novel method to achieve this positioning using poorly aligned examples of a class with no additional labeling. Given a set of unaligned examplars of a class, such as faces, we automatically build an alignment mechanism, without any additional labeling of parts or poses in the data set. Using this alignment mechanism, new members of the class, such as faces resulting from a face detector, can be precisely aligned for the recognition process. Our alignment method improves performance on a face recognition task, both over unaligned images and over images aligned with a face alignment algorithm specifically developed for and trained on hand-labeled face images. We also demonstrate its use on an entirely different class of objects (cars), again without providing any information about parts or pose to the learning algorithm.

A Comparative Study of Local Matching Approach for Face Recognition

Abstract: In contrast to holistic methods, local matching methods extract facial features from different levels of locality and quantify them precisely. To determine how they can be best used for face recognition, we conducted a comprehensive comparative study at each step of the local matching process. The conclusions from our experiments include: (1) additional evidence that Gabor features are effective local feature representations and are robust to illumination changes; (2) discrimination based only on a small portion of the face area is surprisingly good; (3) the configuration of facial components does contain rich discriminating information and comparing corresponding local regions utilizes shape features more effectively than comparing corresponding facial components; (4) spatial multiresolution analysis leads to better classification performance; (5) combining local regions with Borda count classifier combination method alleviates the curse of dimensionality. We implemented a complete face recognition system by integrating the best option of each step. Without training, illumination compensation and without any parameter tuning, it achieves superior performance on every category of the FERET test: near perfect classification accuracy (99.5%) on pictures taken on the same day regardless of indoor illumination variations, and significantly better than any other reported performance on pictures taken several days to more than a year apart. The most significant experiments were repeated on the AR database, with similar results.

Family resemblance: ten family members with prosopagnosia and within-class object agnosia.

Abstract: We report on neuropsychological testing done with a family in which many members reported severe face recognition impairments. These 10 individuals were high functioning in everyday life and performed normally on tests of low-level vision and high-level cognition. In contrast, they showed clear deficits with tests requiring face memory and judgements of facial similarity. They did not show deficits with all aspects of higher level visual processing as all tested performed normally on a challenging facial emotion recognition task and on a global-local letter identification task. On object memory tasks requiring recognition of particular cars and guns, they showed significant deficits so their recognition impairments were not restricted to facial identity. These results strongly suggest the existence of a genetic condition leading to a selective deficit of visual recognition.

Fusing Gabor and LBP feature sets for kernel-based face recognition

Abstract: Extending recognition to uncontrolled situations is a key challenge for practical face recognition systems Finding efficient and discriminative facial appearance descriptors is crucial for this Most existing approaches use features of just one type Here we argue that robust recognition requires several different kinds of appearance information to be taken into account, suggesting the use of heterogeneous feature sets We show that combining two of the most successful local face representations, Gabor wavelets and Local Binary Patterns (LBP), gives considerably better performance than either alone: they are complimentary in the sense that LBP captures small appearance details while Gabor features encode facial shape over a broader range of scales Both feature sets are high dimensional so it is beneficial to use PCA to reduce the dimensionality prior to normalization and integration The Kernel Discriminative Common Vector method is then applied to the combined feature vector to extract discriminant nonlinear features for recognition The method is evaluated on several challenging face datasets including FRGC 104, FRGC 204 and FERET, with promising results

Advances in biometrics : International Conference, ICB 2007 Seoul, Korea, August 27-29, 2007 : proceedings

Abstract: Face Recognition.- Super-Resolved Faces for Improved Face Recognition from Surveillance Video.- Face Detection Based on Multi-Block LBP Representation.- Color Face Tensor Factorization and Slicing for Illumination-Robust Recognition.- Robust Real-Time Face Detection Using Face Certainty Map.- Poster I.- Motion Compensation for Face Recognition Based on Active Differential Imaging.- Face Recognition with Local Gabor Textons.- Speaker Verification with Adaptive Spectral Subband Centroids.- Similarity Rank Correlation for Face Recognition Under Unenrolled Pose.- Feature Correlation Filter for Face Recognition.- Face Recognition by Discriminant Analysis with Gabor Tensor Representation.- Fingerprint Enhancement Based on Discrete Cosine Transform.- Biometric Template Classification: A Case Study in Iris Textures.- Protecting Biometric Templates with Image Watermarking Techniques.- Factorial Hidden Markov Models for Gait Recognition.- A Robust Fingerprint Matching Approach: Growing and Fusing of Local Structures.- Automatic Facial Pose Determination of 3D Range Data for Face Model and Expression Identification.- SVDD-Based Illumination Compensation for Face Recognition.- Keypoint Identification and Feature-Based 3D Face Recognition.- Fusion of Near Infrared Face and Iris Biometrics.- Multi-Eigenspace Learning for Video-Based Face Recognition.- Error-Rate Based Biometrics Fusion.- Online Text-Independent Writer Identification Based on Stroke's Probability Distribution Function.- Arm Swing Identification Method with Template Update for Long Term Stability.- Walker Recognition Without Gait Cycle Estimation.- Comparison of Compression Algorithms' Impact on Iris Recognition Accuracy.- Standardization of Face Image Sample Quality.- Blinking-Based Live Face Detection Using Conditional Random Fields.- Singular Points Analysis in Fingerprints Based on Topological Structure and Orientation Field.- Robust 3D Face Recognition from Expression Categorisation.- Fingerprint Recognition Based on Combined Features.- MQI Based Face Recognition Under Uneven Illumination.- Learning Kernel Subspace Classifier.- A New Approach to Fake Finger Detection Based on Skin Elasticity Analysis.- An Algorithm for Biometric Authentication Based on the Model of Non-Stationary Random Processes.- Identity Verification by Using Handprint.- Reducing the Effect of Noise on Human Contour in Gait Recognition.- Partitioning Gait Cycles Adaptive to Fluctuating Periods and Bad Silhouettes.- Repudiation Detection in Handwritten Documents.- A New Forgery Scenario Based on Regaining Dynamics of Signature.- Curvewise DET Confidence Regions and Pointwise EER Confidence Intervals Using Radial Sweep Methodology.- Bayesian Hill-Climbing Attack and Its Application to Signature Verification.- Wolf Attack Probability: A New Security Measure in Biometric Authentication Systems.- Evaluating the Biometric Sample Quality of Handwritten Signatures.- Outdoor Face Recognition Using Enhanced Near Infrared Imaging.- Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation.- Poster II.- 2^N Discretisation of BioPhasor in Cancellable Biometrics.- Probabilistic Random Projections and Speaker Verification.- On Improving Interoperability of Fingerprint Recognition Using Resolution Compensation Based on Sensor Evaluation.- Demographic Classification with Local Binary Patterns.- Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation.- Fingerprint Matching with an Evolutionary Approach.- Stability Analysis of Constrained Nonlinear Phase Portrait Models of Fingerprint Orientation Images.- Effectiveness of Pen Pressure, Azimuth, and Altitude Features for Online Signature Verification.- Tracking and Recognition of Multiple Faces at Distances.- Face Matching Between Near Infrared and Visible Light Images.- User Classification for Keystroke Dynamics Authentication.- Statistical Texture Analysis-Based Approach for Fake Iris Detection Using Support Vector Machines.- A Novel Null Space-Based Kernel Discriminant Analysis for Face Recognition.- Changeable Face Representations Suitable for Human Recognition.- "3D Face": Biometric Template Protection for 3D Face Recognition.- Quantitative Evaluation of Normalization Techniques of Matching Scores in Multimodal Biometric Systems.- Keystroke Dynamics in a General Setting.- A New Approach to Signature-Based Authentication.- Biometric Fuzzy Extractors Made Practical: A Proposal Based on FingerCodes.- On the Use of Log-Likelihood Ratio Based Model-Specific Score Normalisation in Biometric Authentication.- Predicting Biometric Authentication System Performance Across Different Application Conditions: A Bootstrap Enhanced Parametric Approach.- Selection of Distinguish Points for Class Distribution Preserving Transform for Biometric Template Protection.- Minimizing Spatial Deformation Method for Online Signature Matching.- Pan-Tilt-Zoom Based Iris Image Capturing System for Unconstrained User Environments at a Distance.- Fingerprint Matching with Minutiae Quality Score.- Uniprojective Features for Gait Recognition.- Cascade MR-ASM for Locating Facial Feature Points.- Reconstructing a Whole Face Image from a Partially Damaged or Occluded Image by Multiple Matching.- Robust Hiding of Fingerprint-Biometric Data into Audio Signals.- Correlation-Based Fingerprint Matching with Orientation Field Alignment.- Vitality Detection from Fingerprint Images: A Critical Survey.- Optimum Detection of Multiplicative-Multibit Watermarking for Fingerprint Images.- Fake Finger Detection Based on Thin-Plate Spline Distortion Model.- Robust Extraction of Secret Bits from Minutiae.- Fuzzy Extractors for Minutiae-Based Fingerprint Authentication.- Coarse Iris Classification by Learned Visual Dictionary.- Nonlinear Iris Deformation Correction Based on Gaussian Model.- Shape Analysis of Stroma for Iris Recognition.- Biometric Key Binding: Fuzzy Vault Based on Iris Images.- Multi-scale Local Binary Pattern Histograms for Face Recognition.- Histogram Equalization in SVM Multimodal Person Verification.- Learning Multi-scale Block Local Binary Patterns for Face Recognition.- Horizontal and Vertical 2DPCA Based Discriminant Analysis for Face Verification Using the FRGC Version 2 Database.- Video-Based Face Tracking and Recognition on Updating Twin GMMs.- Poster III.- Fast Algorithm for Iris Detection.- Pyramid Based Interpolation for Face-Video Playback in Audio Visual Recognition.- Face Authentication with Salient Local Features and Static Bayesian Network.- Fake Finger Detection by Finger Color Change Analysis.- Feeling Is Believing: A Secure Template Exchange Protocol.- SVM-Based Selection of Colour Space Experts for Face Authentication.- An Efficient Iris Coding Based on Gauss-Laguerre Wavelets.- Hardening Fingerprint Fuzzy Vault Using Password.- GPU Accelerated 3D Face Registration / Recognition.- Frontal Face Synthesis Based on Multiple Pose-Variant Images for Face Recognition.- Optimal Decision Fusion for a Face Verification System.- Robust 3D Head Tracking and Its Applications.- Multiple Faces Tracking Using Motion Prediction and IPCA in Particle Filters.- An Improved Iris Recognition System Using Feature Extraction Based on Wavelet Maxima Moment Invariants.- Color-Based Iris Verification.- Real-Time Face Detection and Recognition on LEGO Mindstorms NXT Robot.- Speaker and Digit Recognition by Audio-Visual Lip Biometrics.- Modelling Combined Handwriting and Speech Modalities.- A Palmprint Cryptosystem.- On Some Performance Indices for Biometric Identification System.- Automatic Online Signature Verification Using HMMs with User-Dependent Structure.- A Complete Fisher Discriminant Analysis for Based Image Matrix and Its Application to Face Biometrics.- SVM Speaker Verification Using Session Variability Modelling and GMM Supervectors.- 3D Model-Based Face Recognition in Video.- Robust Point-Based Feature Fingerprint Segmentation Algorithm.- Automatic Fingerprints Image Generation Using Evolutionary Algorithm.- Audio Visual Person Authentication by Multiple Nearest Neighbor Classifiers.- Improving Classification with Class-Independent Quality Measures: Q-stack in Face Verification.- Biometric Hashing Based on Genetic Selection and Its Application to On-Line Signatures.- Biometrics Based on Multispectral Skin Texture.- Application of New Qualitative Voicing Time-Frequency Features for Speaker Recognition.- Palmprint Recognition Based on Directional Features and Graph Matching.- Tongue-Print: A Novel Biometrics Pattern.- Embedded Palmprint Recognition System on Mobile Devices.- Template Co-update in Multimodal Biometric Systems.- Continual Retraining of Keystroke Dynamics Based Authenticator.

Locally Linear Regression for Pose-Invariant Face Recognition

Abstract: The variation of facial appearance due to the viewpoint (/pose) degrades face recognition systems considerably, which is one of the bottlenecks in face recognition. One of the possible solutions is generating virtual frontal view from any given nonfrontal view to obtain a virtual gallery/probe face. Following this idea, this paper proposes a simple, but efficient, novel locally linear regression (LLR) method, which generates the virtual frontal view from a given nonfrontal face image. We first justify the basic assumption of the paper that there exists an approximate linear mapping between a nonfrontal face image and its frontal counterpart. Then, by formulating the estimation of the linear mapping as a prediction problem, we present the regression-based solution, i.e., globally linear regression. To improve the prediction accuracy in the case of coarse alignment, LLR is further proposed. In LLR, we first perform dense sampling in the nonfrontal face image to obtain many overlapped local patches. Then, the linear regression technique is applied to each small patch for the prediction of its virtual frontal patch. Through the combination of all these patches, the virtual frontal view is generated. The experimental results on the CMU PIE database show distinct advantage of the proposed method over Eigen light-field method.

Real-Time Face Detection and Motion Analysis With Application in “Liveness” Assessment

Abstract: A robust face detection technique along with mouth localization, processing every frame in real time (video rate), is presented. Moreover, it is exploited for motion analysis onsite to verify "liveness" as well as to achieve lip reading of digits. A methodological novelty is the suggested quantized angle features ("quangles") being designed for illumination invariance without the need for preprocessing (e.g., histogram equalization). This is achieved by using both the gradient direction and the double angle direction (the structure tensor angle), and by ignoring the magnitude of the gradient. Boosting techniques are applied in a quantized feature space. A major benefit is reduced processing time (i.e., that the training of effective cascaded classifiers is feasible in very short time, less than 1 h for data sets of order 104). Scale invariance is implemented through the use of an image scale pyramid. We propose "liveness" verification barriers as applications for which a significant amount of computation is avoided when estimating motion. Novel strategies to avert advanced spoofing attempts (e.g., replayed videos which include person utterances) are demonstrated. We present favorable results on face detection for the YALE face test set and competitive results for the CMU-MIT frontal face test set as well as on "liveness" verification barriers.

Illumination Invariant Face Recognition: A Survey

Abstract: The illumination variation problem is one of the well-known problems in face recognition in uncontrolled environment. In this paper an extensive and up-to-date survey of the existing techniques to address this problem is presented. This survey covers the passive techniques that attempt to solve the illumination problem by studying the visible light images in which face appearance has been altered by varying illumination, as well as the active techniques that aim to obtain images of face modalities invariant to environmental illumination.

Interactive Photo Annotation Based on Face Clustering

Abstract: An interactive photo annotation method uses clustering based on facial similarities to improve annotation experience. The method uses a face recognition algorithm to extract facial features of a photo album and cluster the photos into multiple face groups based on facial similarity. The method annotates a face group collectively using annotations, such as name identifiers, in one operation. The method further allows merging and splitting of face groups. Special graphical user interfaces, such as displays in a group view area and a thumbnail area and drag-and-drop features, are used to further improve the annotation experience.