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

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Massimo Tistarelli, Josef Bigun, Anil Jain
Springer Science & Business Media, May 17, 2002 - Computers - 202 pages
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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.
 

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Contents

An Incremental Learning Algorithm for Face
1
Face Recognition Based on ICA Combined with FLD
10
Understanding Iconic ImageBased Face Biometrics
19
Fusion of LDA and PCA for Face Verification
30
Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment
38
Fingerprint Matching Using Feature Space Correlation
48
A Constructive Definition
58
Pseudoentropy Similarity for Human Biometrics
67
Coarse to Fine Face Detection Based on Skin Color Adaption
112
Robust Face Recognition Using Dynamic Space Warping
121
Subspace Classification for Face Recognition
133
Gait Appearance for Recognition
142
Viewinvariant Estimation of Height and Stride for Gait Recognition
155
Improvement of Online Signature Verification System Robust to Intersession Variability
168
Biometric Identification in Forensic Cases According to the Bayesian Approach
177
A New Quadratic Classifier Applied to Biometric Recognition
186

Mental Characteristics of Person as Basic Biometrics
78
Detection of Frontal Faces in Video Streams
91
Genetic Model Optimization for Hausdorff DistanceBased Face Localization
103

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