Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overview of pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.

/pdf/data-clustering-a-review-4c452f5cab.pdf

Data clustering: a review

A major new professional reference work on fingerprint security systems and technology from leading international researchers in the field Handbook provides authoritative and comprehensive coverage of all major topics, concepts, and methods for fingerprint security systems This unique reference work is an absolutely essential resource for all biometric security professionals, researchers, and systems administrators

/pdf/handbook-of-fingerprint-recognition-16mh887j24.pdf

Handbook of Fingerprint Recognition

In order to ensure that the performance of an automatic fingerprint identification/verification system will be robust with respect to the quality of input fingerprint images, it is essential to incorporate a fingerprint enhancement algorithm in the minutiae extraction module. We present a fast fingerprint enhancement algorithm, which can adaptively improve the clarity of ridge and valley structures of input fingerprint images based on the estimated local ridge orientation and frequency. We have evaluated the performance of the image enhancement algorithm using the goodness index of the extracted minutiae and the accuracy of an online fingerprint verification system. Experimental results show that incorporating the enhancement algorithm improves both the goodness index and the verification accuracy.

/pdf/fingerprint-image-enhancement-algorithm-and-performance-405diava9j.pdf

Fingerprint image enhancement: algorithm and performance evaluation

The objective of this paper is to give a comprehensive introduction to applied cryptography with an engineer or computer scientist in mind. The emphasis is on the knowledge needed to create practical systems which supports integrity, confidentiality, or authenticity. Topics covered includes an introduction to the concepts in cryptography, attacks against cryptographic systems, key use and handling, random bit generation, encryption modes, and message authentication codes. Recommendations on algorithms and further reading is given in the end of the paper. This paper should make the reader able to build, understand and evaluate system descriptions and designs based on the cryptographic components described in the paper.

[서평]「Applied Cryptography」

IEEE Transactions on Pattern Analysis and Machine Intelligence

Iris.- Iris Recognition at a Distance.- Iris Recognition Using Fourier-Wavelet Features.- Specific Texture Analysis for Iris Recognition.- A Study on Iris Image Restoration.- Face I (Short).- Eye Perturbation Approach for Robust Recognition of Inaccurately Aligned Faces.- On Combining Textural and Geometrical Scores for Discriminative Face Authentication.- A One Bit Facial Asymmetry Code (FAC) in Fourier Domain for Human Recognition.- Face Recognition with the Multiple Constrained Mutual Subspace Method.- A Flexible Object Model for Recognising and Synthesising Facial Expressions.- Face Reconstruction Across Different Poses and Arbitrary Illumination Conditions.- Stepwise Reconstruction of High-Resolution Facial Image Based on Interpolated Morphable Face Model.- Illumination Invariant Face Recognition Using Linear Combination of Face Exemplars.- Video-Based Face Recognition Using Bayesian Inference Model.- Finger-I (Short).- A Hybrid Swipe Fingerprint Mosaicing Scheme.- A Study on Multi-unit Fingerprint Verification.- A Fingerprint Authentication System Based on Mobile Phone.- Fingerprint Quality Indices for Predicting Authentication Performance.- Registration of Fingerprints by Complex Filtering and by 1D Projections of Orientation Images.- A Feature Map Consisting of Orientation and Inter-ridge Spacing for Fingerprint Retrieval.- A Graph Matching Based Approach to Fingerprint Classification Using Directional Variance.- Fingerprint Singular Points Detection and Direction Estimation with a "T" Shape Model.- Face-I.- Face Detection Based on the Manifold.- Local and Global Feature Extraction for Face Recognition.- Video-Based Face Recognition Using Earth Mover's Distance.- Face Recognition Based on Recursive Bayesian Fusion of Multiple Signals and Results from Expert Classifier Sets.- Fingerprint.- Principal Deformations of Fingerprints.- Fingerprint Mosaicking by Rolling and Sliding.- Minutiae Matching Based Fingerprint Verification Using Delaunay Triangulation and Aligned-Edge-Guided Triangle Matching.- Security and Smartcard.- An Asymmetric Fingerprint Matching Algorithm for Java CardTM.- Scenario Based Performance Optimisation in Face Verification Using Smart Cards.- Characterization, Similarity Score and Uniqueness Associated with Perspiration Pattern.- Fuzzy Vault for Fingerprints.- Short Oral.- Infrared Face Recognition by Using Blood Perfusion Data.- On Finding Differences Between Faces.- Face Detection Using Look-Up Table Based Gentle AdaBoost.- Post-processing on LDA's Discriminant Vectors for Facial Feature Extraction.- An Integrated Prediction Model for Biometrics.- Active Shape Models with Invariant Optimal Features (IOF-ASMs).- Palmprint Authentication Using Time Series.- Ear Biometrics by Force Field Convergence.- Short Oral-4.- Towards Scalable View-Invariant Gait Recognition: Multilinear Analysis for Gait.- Combining Verification Decisions in a Multi-vendor Environment.- Gait Recognition by Combining Classifiers Based on Environmental Contexts.- Addressing the Vulnerabilities of Likelihood-Ratio-Based Face Verification.- Practical Biometric Authentication with Template Protection.- A Study of Brute-Force Break-ins of a Palmprint Verification System.- Modification of Intersession Variability in On-Line Signature Verifier.- MOC via TOC Using a Mobile Agent Framework.- Fusion.- Improving Fusion with Margin-Derived Confidence in Biometric Authentication Tasks.- A Classification Approach to Multi-biometric Score Fusion.- A Generic Protocol for Multibiometric Systems Evaluation on Virtual and Real Subjects.- Multi-modal.- Multi-biometrics 2D and 3D Ear Recognition.- Biometric Authentication System Using Reduced Joint Feature Vector of Iris and Face.- An On-Line Signature Verification System Based on Fusion of Local and Global Information.- Human Recognition at a Distance in Video by Integrating Face Profile and Gait.- 50 - 5:50 Oral-7 (Palm and Finger Surface).- Identity Verification Utilizing Finger Surface Features.- Palmprint Authentication Based on Orientation Code Matching.- A Novel Palm-Line Detector.- Speaker and Gait.- A New On-Line Model Quality Evaluation Method for Speaker Verification.- Improving Speaker Verification Using ALISP-Based Specific GMMs.- Multimodal Speaker Verification Using Ear Image Features Extracted by PCA and ICA.- Modelling the Time-Variant Covariates for Gait Recognition.- Face II.- Robust Face Recognition Using Advanced Correlation Filters with Bijective-Mapping Preprocessing.- Photometric Normalisation for Face Verification.- Experimental Evaluation of Eye Location Accuracies and Time-Lapse Effects on Face Recognition Systems.- Experiments in Mental Face Retrieval.- Poster I.- Fingerprint Image Segmentation Based on Quadric Surface Model.- A Fingerprint Matching Algorithm Based on Radial Structure and a Structure-Rewarding Scoring Strategy.- A Novel Algorithm for Distorted Fingerprint Matching Based on Fuzzy Features Match.- Minutiae Quality Scoring and Filtering Using a Neighboring Ridge Structural Analysis on a Thinned Fingerprint Image.- Hardware-Software Codesign of a Fingerprint Identification Algorithm.- Fingerprint Matching Using the Distribution of the Pairwise Distances Between Minutiae.- A Layered Fingerprint Recognition Method.- Super-template Generation Using Successive Bayesian Estimation for Fingerprint Enrollment.- Secure Fingerprint Matching with External Registration.- Palmprint Recognition Using Fourier-Mellin Transformation Based Registration Method.- Parametric Versus Non-parametric Models of Driving Behavior Signals for Driver Identification.- Performance Evaluation and Prediction for 3D Ear Recognition.- Optimal User Weighting Fusion in DWT Domain On-Line Signature Verification.- Gait Recognition Using Spectral Features of Foot Motion.- VALID: A New Practical Audio-Visual Database, and Comparative Results.- Audio-Visual Speaker Identification via Adaptive Fusion Using Reliability Estimates of Both Modalities.- Speaker Identification Using the VQ-Based Discriminative Kernels.- Exploiting Glottal Information in Speaker Recognition Using Parallel GMMs.- Biometric Recognition Using Feature Selection and Combination.- Evaluation of Biometric Identification in Open Systems.- Exploring Similarity Measures for Biometric Databases.- Indexing Biometric Databases Using Pyramid Technique.- Classification Enhancement via Biometric Pattern Perturbation.- Calculation of a Composite DET Curve.- Privacy Operating Characteristic for Privacy Protection in Surveillance Applications.- Poster II.- Headprint - Person Reacquisition Using Visual Features of Hair in Overhead Surveillance Video.- A Survey of 3D Face Recognition Methods.- Influences of Image Disturbances on 2D Face Recognition.- Local Feature Based 3D Face Recognition.- Fusion of Appearance and Depth Information for Face Recognition.- Gabor Feature Based Classification Using 2D Linear Discriminant Analysis for Face Recognition.- Multi-resolution Histograms of Local Variation Patterns (MHLVP) for Robust Face Recognition.- Analysis of Response Performance Characteristics for Identification Using a Matching Score Generation Model.- Pose Invariant Face Recognition Under Arbitrary Illumination Based on 3D Face Reconstruction.- Discriminant Analysis Based on Kernelized Decision Boundary for Face Recognition.- A Probabilistic Approach to Semantic Face Retrieval System.- Authenticating Corrupted Facial Images on Stand-Alone DSP System.- Evaluation of 3D Face Recognition Using Registration and PCA.- Dynamic Approach for Face Recognition Using Digital Image Skin Correlation.- Rank-Based Decision Fusion for 3D Shape-Based Face Recognition.- Robust Active Shape Model Construction and Fitting for Facial Feature Localization.- Comparative Assessment of Content-Based Face Image Retrieval in Different Color Spaces.- A Principled Approach to Score Level Fusion in Multimodal Biometric Systems.- A Score-Level Fusion Benchmark Database for Biometric Authentication.- Fusion for Multimodal Biometric Identification.- Between-Source Modelling for Likelihood Ratio Computation in Forensic Biometric Recognition.- The Effectiveness of Generative Attacks on an Online Handwriting Biometric.- Vulnerabilities in Biometric Encryption Systems.- Securing Electronic Medical Records Using Biometric Authentication.- A Novel Approach to Combining Client-Dependent and Confidence Information in Multimodal Biometrics.

Audio- and Video-Based Biometric Person Authentication : 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005. Proceedings

Often deep learning methods are associated with huge amounts of training data. The deeper the network gets, the larger is the need for training data. A large amount of labeled data helps the network learn about the variations it needs to handle in the prediction stage. It is not easy for everyone to get access to huge amounts of labeled data leaving a few to have the luxury to design very deep networks. In this paper, we propose to flatten the disparity by using the modeling methods to minimize the need for huge amounts of data for training a deep network. Using face recognition as an example, we demonstrate how limited labeled data can be leveraged to obtain near state of the art performance with generalization capability across multiple databases. In addition, we show that the normalization in the overall network can improve the speed and resource requirement for the prediction/inferencing stage.

Learning face recognition from limited training data using deep neural networks

Cancelable biometric systems are gaining in popularity for use in person authentication for applications where the privacy and security of biometric templates are important considerations. A variety of approaches have been proposed in the literature. In this work, we have chosen two (a registration based and a registration free) techniques and performed a comparative study focusing on template representation size, useful dataset coverage, system accuracy and transform strength. Results show that both systems have their own advantages that are suited for use in specific applications.

Comparative analysis of registration based and registration free methods for cancelable fingerprint biometrics

Techniques for generating a distorted fingerprint representation for a given fingerprint image are provided First, at least one fingerprint feature point from a given fingerprint image is selected At least one representation of a region proximate to the selected fingerprint feature point is then generated Next, the representation of the region proximate to the selected fingerprint feature point is distorted The distortion comprises applying a random projection to the representation to generate a randomly projected representation of the region proximate to the selected fingerprint feature point A distorted template is then formed, wherein the distorted template comprises the randomly projected representation of the region proximate to the selected fingerprint feature point

Methods and Apparatus for Generation of Cancelable Fingerprint Template

A scalable parallel algorithm for edge detection and surface reconstruction is presented. The algorithm is based on fitting a weak membrane to the pixel gray valves by minimizing the associated energy functional. The edge detection process is modeled as a line process and used as a constraint in minimizing the energy functional of the image. The optimal edge assignment cannot be obtained directly as the energy function is non-convex. Using graduated non-convexity (GNC) approach, the energy is minimized. The proposed parallel algorithm has been implemented on a cluster of workstations using the PVM communication library. The results of parallel implementation on synthetic and natural images are presented. The speedup is observed to be near-linear, thus providing scalability with the problem size. The parallel processing approach presented here can be extended to solve similar problems (e.g., image restoration, and image compression) which use regularization techniques.

Nalini K. Ratha

Papers

Audio- and Video-Based Biometric Person Authentication : 5th International Conference, AVBPA 2005, Hilton Rye Town, NY, USA, July 20-22, 2005. Proceedings

Learning face recognition from limited training data using deep neural networks

Comparative analysis of registration based and registration free methods for cancelable fingerprint biometrics

Methods and Apparatus for Generation of Cancelable Fingerprint Template

A distributed edge detection and surface reconstruction algorithm