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.

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

Poster Session 4: Face/Gesture/Action Detection and Recognition.- Palmprint Recognition Under Unconstrained Scenes.- Comparative Studies on Multispectral Palm Image Fusion for Biometrics.- Learning Gabor Magnitude Features for Palmprint Recognition.- Sign Recognition Using Constrained Optimization.- Poster Session 4: Image and Video Processing.- Depth from Stationary Blur with Adaptive Filtering.- Three-Stage Motion Deblurring from a Video.- Near-Optimal Mosaic Selection for Rotating and Zooming Video Cameras.- Video Mosaicing Based on Structure from Motion for Distortion-Free Document Digitization.- Super Resolution of Images of 3D Scenecs.- Learning-Based Super-Resolution System Using Single Facial Image and Multi-resolution Wavelet Synthesis.- Poster Session 4: Segmentation and Classification.- Statistical Framework for Shot Segmentation and Classification in Sports Video.- Sports Classification Using Cross-Ratio Histograms.- A Bayesian Network for Foreground Segmentation in Region Level.- Efficient Graph Cuts for Multiclass Interactive Image Segmentation.- Feature Subset Selection for Multi-class SVM Based Image Classification.- Evaluating Multi-class Multiple-Instance Learning for Image Categorization.- Poster Session 4: Shape.- TransforMesh : A Topology-Adaptive Mesh-Based Approach to Surface Evolution.- Microscopic Surface Shape Estimation of a Transparent Plate Using a Complex Image.- Shape Recovery from Turntable Image Sequence.- Shape from Contour for the Digitization of Curved Documents.- Improved Space Carving Method for Merging and Interpolating Multiple Range Images Using Information of Light Sources of Active Stereo.- Shape Representation and Classification Using Boundary Radius Function.- A Convex Programming Approach to the Trace Quotient Problem.- Learning a Fast Emulator of a Binary Decision Process.- Multiplexed Illumination for Measuring BRDF Using an Ellipsoidal Mirror and a Projector.- Analyzing the Influences of Camera Warm-Up Effects on Image Acquisition.- Simultaneous Plane Extraction and 2D Homography Estimation Using Local Feature Transformations.- A Fast Optimal Algorithm for L 2 Triangulation.- Adaptively Determining Degrees of Implicit Polynomial Curves and Surfaces.- Determining Relative Geometry of Cameras from Normal Flows.- Poster Session 5: Geometry.- Highest Accuracy Fundamental Matrix Computation.- Sequential L ??? Norm Minimization for Triangulation.- Initial Pose Estimation for 3D Model Tracking Using Learned Objective Functions.- Multiple View Geometry for Non-rigid Motions Viewed from Translational Cameras.- Visual Odometry for Non-overlapping Views Using Second-Order Cone Programming.- Pose Estimation from Circle or Parallel Lines in a Single Image.- An Occupancy-Depth Generative Model of Multi-view Images.- Poster Session 5: Matching and Registration.- Image Correspondence from Motion Subspace Constraint and Epipolar Constraint.- Efficient Registration of Aerial Image Sequences Without Camera Priors.- Simultaneous Appearance Modeling and Segmentation for Matching People Under Occlusion.- Content-Based Matching of Videos Using Local Spatio-temporal Fingerprints.- Automatic Range Image Registration Using Mixed Integer Linear Programming.- Accelerating Pattern Matching or How Much Can You Slide?.- Poster Session 5: Recognition.- Detecting, Tracking and Recognizing License Plates.- Action Recognition for Surveillance Applications Using Optic Flow and SVM.- The Kernel Orthogonal Mutual Subspace Method and Its Application to 3D Object Recognition.- Viewpoint Insensitive Action Recognition Using Envelop Shape.- Unsupervised Identification of Multiple Objects of Interest from Multiple Images: dISCOVER.- Poster Session 5: Stereo, Range and 3D.- Fast 3-D Interpretation from Monocular Image Sequences on Large Motion Fields.- Color-Stripe Structured Light Robust to Surface Color and Discontinuity.- Stereo Vision Enabling Precise Border Localization Within a Scanline Optimization Framework.- Three Dimensional Position Measurement for Maxillofacial Surgery by Stereo X-Ray Images.- Total Absolute Gaussian Curvature for Stereo Prior.- Fast Optimal Three View Triangulation.- Stereo Matching Using Population-Based MCMC.- Dense 3D Reconstruction of Specular and Transparent Objects Using Stereo Cameras and Phase-Shift Method.- Identifying Foreground from Multiple Images.- Image and Video Matting with Membership Propagation.- Temporal Priors for Novel Video Synthesis.- Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees.- Poster Session 6: Face/Gesture/Action Detection and Recognition.- Analyzing Facial Expression by Fusing Manifolds.- A Novel Multi-stage Classifier for Face Recognition.- Discriminant Clustering Embedding for Face Recognition with Image Sets.- Privacy Preserving: Hiding a Face in a Face.- Face Mosaicing for Pose Robust Video-Based Recognition.- Face Recognition by Using Elongated Local Binary Patterns with Average Maximum Distance Gradient Magnitude.- An Adaptive Nonparametric Discriminant Analysis Method and Its Application to Face Recognition.- Discriminating 3D Faces by Statistics of Depth Differences.- Kernel Discriminant Analysis Based on Canonical Differences for Face Recognition in Image Sets.- Person-Similarity Weighted Feature for Expression Recognition.- Converting Thermal Infrared Face Images into Normal Gray-Level Images.- Recognition of Digital Images of the Human Face at Ultra Low Resolution Via Illumination Spaces.- Poster Session 6: Math for Vision.- Crystal Vision-Applications of Point Groups in Computer Vision.- On the Critical Point of Gradient Vector Flow Snake.- A Fast and Noise-Tolerant Method for Positioning Centers of Spiraling and Circulating Vector Fields.- Interpolation Between Eigenspaces Using Rotation in Multiple Dimensions.- Conic Fitting Using the Geometric Distance.- Poster Session 6: Segmentation and Classification.- Efficiently Solving the Fractional Trust Region Problem.- Image Segmentation Using Iterated Graph Cuts Based on Multi-scale Smoothing.- Backward Segmentation and Region Fitting for Geometrical Visibility Range Estimation.- Image Segmentation Using Co-EM Strategy.- Co-segmentation of Image Pairs with Quadratic Global Constraint in MRFs.- Shape Reconstruction from Cast Shadows Using Coplanarities and Metric Constraints.- Evolving Measurement Regions for Depth from Defocus.- A New Framework for Grayscale and Colour Non-lambertian Shape-from-Shading.- A Regularized Approach to Feature Selection for Face Detection.- Iris Tracking and Regeneration for Improving Nonverbal Interface.- Face Mis-alignment Analysis by Multiple-Instance Subspace.

Computer vision - ACCV 2007 : 8th Asian Conference on Computer Vision, Tokyo, Japan, November 18-22, 2007, proceedings

Facial expression recognition has been intensively studied for decades, notably by the psychology community and more recently the pattern recognition community. What is more challenging, and the subject of more recent research, is the problem of recognizing subtle emotions exhibited by so-called micro-expressions. Recognizing a micro-expression is substantially more challenging than conventional expression recognition because these micro-expressions are only temporally exhibited in a fraction of a second and involve minute spatial changes. Until now, work in this field is at a nascent stage, with only a few existing micro-expression databases and methods. In this article, we propose a new micro-expression recognition approach based on the Eulerian motion magnification technique, which could reveal the hidden information and accentuate the subtle changes in micro-expression motion. Validation of our proposal was done on the recently proposed CASME II dataset in comparison with baseline and state-of-the-art methods. We achieve a good recognition accuracy of up to 75.30 % by using leave-one-out cross validation evaluation protocol. Extensive experiments on various factors at play further demonstrate the effectiveness of our proposed approach.

/pdf/effective-recognition-of-facial-micro-expressions-with-video-15yedwb15t.pdf

Effective recognition of facial micro-expressions with video motion magnification

The local binary pattern (LBP) and its variants have shown the effectiveness in texture images classification, face recognition, and other applications However, most of these LBP methods only focus on the histogram of LBP patterns and ignore the spatial contextual information between LBP patterns In this paper, we propose a 2D-LBP method which uses a sliding window to count the weighted occurrence number of the rotation invariant uniform LBP pattern pairs to obtain the spatial contextual information The multi-resolution 2D-LBP features can also be obtained when the radius of 2D-LBP is changed At last, a two-stage classifier which acts as an ensemble learning step is followed to achieve an accurate classification by combining the predictions on each 2D-LBP with single resolution Theoretical proof shows that the proposed 2D-LBP is a general framework and can be integrated on other LBP variants to derive new feature extraction methods Experimental results show that, the proposed method achieves 9971%, 9709%, 9848%, and 4900% classification accuracy on the public “Brodatz,” “CUReT,” “UIUC,” and “FMD” texture image databases, respectively Compared with the original LBP and its variants, the proposed method obtains higher classification accuracy under different cases, and simultaneously owns shorter time complexity

2D-LBP: An Enhanced Local Binary Feature for Texture Image Classification

Identity recognition using local features extracted from ear images has recently attracted a great deal of attention in the intelligent biometric systems community. The rich and reliable information of the human ear and its stable structure over a long period of time present ear recognition technology as an appealing choice for identifying individuals and verifying their identities. This paper considers the ear recognition problem using local binary patterns (LBP) features. Where, the LBP-like features characterize the spatial structure of the image texture based on the assumption that this texture has a pattern and its strength (amplitude)-two locally complementary aspects. Their high discriminative power, invariance to monotonic gray-scale changes and computational efficiency properties make the LBP-like features suitable for the ear recognition problem. Thus, the performance of several recent LBP variants introduced in the literature as feature extraction techniques is investigated to determine how can they be best utilized for ear recognition. To this end, we carry out a comprehensive comparative study on the identification and verification scenarios separately. Besides, a new variant of the traditional LBP operator named averaged local binary patterns (ALBP) is proposed and its ability in representing texture of ear images is compared with the other LBP variants. The ear identification and verification experiments are extensively conducted on five publicly available constrained and unconstrained benchmark ear datasets stressing various imaging conditions; namely IIT Delhi (I), IIT Delhi (II), AMI, WPUT and AWE. The obtained results for both identification and verification indicate that the current LBP texture descriptors are successful feature extraction candidates for ear recognition systems in the case of constrained imaging conditions and can achieve recognition rates reaching up to 99%; while, their performance faces difficulties when the level of distortions increases. Moreover, it is noted that the tested LBP variants achieve almost close performance on ear recognition. Thus, further studies on other applications are needed to verify this close performance. We believe that the presented study has significant insights and can benefit researchers in choosing between LBP variants as well as acting as a connection between previous studies and future work in utilizing LBP-like features in ear recognition systems.

Ear recognition using local binary patterns: A comparative experimental study

In this paper, we propose a novel variant of Local Binary Patterns (LBP) so-called Elliptical Local Binary Patterns (ELBP) for face recognition. In ELBP, we use horizontal and vertical ellipse patterns to capture micro facial feature of face images in both horizontal and vertical directions. ELBP is applied in face recognition with dimension reduction step by Whitened Principal Component Analysis (WPCA). Our experiment results upon AR, FERET and Surveillance Cameras Face (SCface) databases prove the advantages of ELBP over LBP for face recognition under different conditions and with ELBP WPCA we can get very remarkable results.

Elliptical local binary patterns for face recognition

We present a novel feature extraction method named local patterns of gradients (LPOGs) for robust face recognition. LPOG uses block-wised elliptical local binary patterns (BELBP), a refined variant of ELBP, and local phase quantization (LPQ) operators directly on gradient images for capturing local texture patterns to build up a feature vector of a face image. From one input image, two directional gradient images are computed. A symmetric pair of BELBP and a LPQ operator are then separately applied upon each gradient image to generate local patterns images. Histogram sequences of local patterns images’ nonoverlapped subregions are finally concatenated to form the LPOG vector for the given image. Based on LPOG descriptor, we propose a novel face recognition system which exploits whitened principal component analysis (WPCA) for dimension reduction and weighted angle-based distance for classification. Experimental results on three large public databases (FERET, AR, and SCface) prove that LPOG WPCA system is robust against a wide range of challenges, such as illumination, expression, occlusion, pose, time-lapse variations, and low resolution. In addition, comparison with other systems shows that LPOG WPCA significantly outperforms the state-of-the-art methods. Computationally, timing benchmarks also demonstrate that our LPOG method is faster than many advanced feature extraction algorithms and can be applied in real-world applications.

Local Patterns of Gradients for Face Recognition

Centered around feature extraction, the core task of any Face recognition system, our objective is devising a robust facial representation against major challenges, such as variations of illumination, pose and time-lapse and low resolution probe images, to name a few. Besides, fast processing speed is another crucial criterion. Towards these ends, several methods have been proposed through out this thesis. Firstly, based on the orientation characteristics of the facial information and important features, like the eyes and mouth, a novel variant of LBP, referred as ELBP, is designed for encoding micro patterns with the usage of an horizontal ellipse sample. Secondly, ELBP is exploited to extract local features from oriented edge magnitudes images. By this, the Elliptical Patterns of Oriented Edge Magnitudes (EPOEM) description is built. Thirdly, we propose a novel feature extraction method so called Patch based Local Phase Quantization of Monogenic components (PLPQMC). Lastly, a robust facial representation namely Local Patterns of Gradients (LPOG) is developed to capture meaningful features directly from gradient images. Chiefs among these methods are PLPQMC and LPOG as they are per se illumination invariant and blur tolerant. Impressively, our methods, while offering comparable or almost higher results than that of existing systems, have low computational cost and are thus feasible to deploy in real life applications.

/pdf/contributions-to-facial-feature-extraction-for-face-fmol4nnmev.pdf

Contributions to facial feature extraction for face recognition

This paper presents improvements for face recognition methods that use LBP descriptor as a main technique in encoding micro features of face images. Our improvements are focused on the feature extraction and dimension reduction steps. In feature extraction, we use a variant of Local Binary Pattern (LBP) so-called Elliptical Local Binary Pattern (ELBP), which is more efficient than LBP for extracting micro facial features of the human face. ELBP of one pixel is built by thresholding its gray value with its P neighboring pixels on a horizontal ellipse. ELBP operator is applied in Pattern of Oriented Edge Magnitudes (POEM) to build Elliptical POEM (EPOEM) descriptor. The dimension reduction step is conducted by using Singular Value Decomposition (SVD) based Whitened Principal Component Analysis (WPCA). For performance evaluation of our improvements, we compare them with LBP based, POEM based approaches and other popular face recognition systems. The experimental results on state-of-the-art FERET and AR face databases prove the advantages and effectiveness of our improvements.

How far we can improve micro features based face recognition systems

In this paper, the main contribution is to explore three different types of features including Exchangeable Image File (EXIF) features, handcrafted features and learned features in order to address the problem of large field/close up images classification with a Support Vector Machine (SVM) classifier. The impacts of every feature set on classification performances and computational complexities are investigated and compared to each other. Results prove that learned features are of course very efficient but with a computational cost that might be unreasonable. On the contrary, it appears that it is worthy to consider EXIF features when available because they represent a very good compromise between accuracy and computational cost.

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Huu-Tuan Nguyen

Papers

Elliptical local binary patterns for face recognition

Local Patterns of Gradients for Face Recognition

Contributions to facial feature extraction for face recognition

How far we can improve micro features based face recognition systems

Large Field/Close-Up Image Classification: From Simple to Very Complex Features