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Biometric Authentication: International ECCV 2002 Workshop Copenhagen, Denmark, June 1, 2002 Proceedings

✍ Scribed by O. Déniz, M. Castrillón, J. Lorenzo, M. Hernández (auth.), Massimo Tistarelli, Josef Bigun, Anil K. Jain (eds.)


Publisher
Springer-Verlag Berlin Heidelberg
Year
2002
Tongue
English
Leaves
202
Series
Lecture Notes in Computer Science 2359
Edition
1
Category
Library

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✦ Synopsis


Biometric authentication refers to identifying an individual based on his or her distinguishing physiological and/or behavioral characteristics. It associates an individual with a previously determined identity based on that individual s appearance or behavior. Because many physiological or behavioral characteristics (biometric indicators) are distinctive to each person, biometric identifiers are inherently more reliable and more capable than knowledge-based (e.g., password) and token-based (e.g., a key) techniques in differentiating between an authorized person and a fraudulent impostor. For this reason, more and more organizations are looking to automated identity authentication systems to improve customer satisfaction, security, and operating efficiency as well as to save critical resources. Biometric authentication is a challenging pattern recognition problem; it involves more than just template matching. The intrinsic nature of biometric data must be carefully studied, analyzed, and its properties taken into account in developing suitable representation and matching algorithms. The intrinsic variability of data with time and environmental conditions, the social acceptability and invasiveness of acquisition devices, and the facility with which the data can be counterfeited must be considered in the choice of a biometric indicator for a given application. In order to deploy a biometric authentication system, one must consider its reliability, accuracy, applicability, and efficiency. Eventually, it may be necessary to combine several biometric indicators (multimodal-biometrics) to cope with the drawbacks of the individual biometric indicators.

✦ Table of Contents


An Incremental Learning Algorithm for Face Recognition....Pages 1-9
Face Recognition Based on ICA Combined with FLD....Pages 10-18
Understanding Iconic Image-Based Face Biometrics....Pages 19-29
Fusion of LDA and PCA for Face Verification....Pages 30-37
Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment....Pages 39-47
Fingerprint Matching Using Feature Space Correlation....Pages 48-57
Fingerprint Minutiae: A Constructive Definition....Pages 58-66
Pseudo-entropy Similarity for Human Biometrics....Pages 67-77
Mental Characteristics of Person as Basic Biometrics....Pages 78-89
Detection of Frontal Faces in Video Streams....Pages 91-102
Genetic Model Optimization for Hausdorff Distance-Based Face Localization....Pages 103-111
Coarse to Fine Face Detection Based on Skin Color Adaption....Pages 112-120
Robust Face Recognition Using Dynamic Space Warping....Pages 121-132
Subspace Classification for Face Recognition....Pages 133-141
Gait Appearance for Recognition....Pages 143-154
View-invariant Estimation of Height and Stride for Gait Recognition....Pages 155-167
Improvement of On-line Signature Verification System Robust to Intersession Variability....Pages 168-175
Biometric Identification in Forensic Cases According to the Bayesian Approach....Pages 177-185
A New Quadratic Classifier Applied to Biometric Recognition....Pages 186-196

✦ Subjects


Pattern Recognition; Image Processing and Computer Vision; Document Preparation and Text Processing; Computers and Society; Management of Computing and Information Systems; Bioinformatics


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