Biometric Authentication

1. Front Matter

   Pages I-X

   PDF

2. Face Recognition I

   1. An Incremental Learning Algorithm for Face Recognition

      • O. Déniz, M. Castrillón, J. Lorenzo, M. Hernández

      Pages 1-9

   2. Face Recognition Based on ICA Combined with FLD

      • Juneho Yi, Jongsun Kim, Jongmoo Choi, Junghyun Han, Eunseok Lee

      Pages 10-18

   3. Understanding Iconic Image-Based Face Biometrics

      • Massimo Tistarelli, Andrea Lagorio, Enrico Grosso

      Pages 19-29

   4. Fusion of LDA and PCA for Face Verification

      • Gian Luca Marcialis, Fabio Roli

      Pages 30-37

3. Fingerprint Recognition

   1. Complex Filters Applied to Fingerprint Images Detecting Prominent Symmetry Points Used for Alignment

      • Kenneth Nilsson, Josef Bigun

      Pages 39-47

   2. Fingerprint Matching Using Feature Space Correlation

      • Arun Ross, James Reisman, Anil Jain

      Pages 48-57

   3. Fingerprint Minutiae: A Constructive Definition

      • Ruud M. Bolle, Andrew W. Senior, Nalini K. Ratha, Sharath Pankanti

      Pages 58-66

4. Psychology and Biometrics

   1. Pseudo-entropy Similarity for Human Biometrics

      • Leonid Kompanets, Janusz Bobulski, Roman Wyrzykowski

      Pages 67-77

   2. Mental Characteristics of Person as Basic Biometrics

      • Tetiana Valchuk, Roman Wyrzykowski, Leonid Kompanets

      Pages 78-89

5. Face Detection and Localization

   1. Detection of Frontal Faces in Video Streams

      • M. Castrillón Santana, J. Lorenzo Navarro, J. Cabrera Gámez, F. M. Hernández Tejera, J. Méndez Rodríguez

      Pages 91-102

   2. Genetic Model Optimization for Hausdorff Distance-Based Face Localization

      • Klaus J. Kirchberg, Oliver Jesorsky, Robert W. Frischholz

      Pages 103-111

   3. Coarse to Fine Face Detection Based on Skin Color Adaption

      • Hichem Sahbi, Nozha Boujemaa

      Pages 112-120

6. Face Recognition II

   1. Robust Face Recognition Using Dynamic Space Warping

      • Hichem Sahbi, Nozha Boujemaa

      Pages 121-132

   2. Subspace Classification for Face Recognition

      • Raffaele Cappelli, Dario Maio, Davide Maltoni

      Pages 133-141

7. Gait and Signature Analysis

   1. Gait Appearance for Recognition

      • L. Lee, W. E. L. Grimson

      Pages 143-154

   2. View-invariant Estimation of Height and Stride for Gait Recognition

      • Chiraz BenAbdelkader, Ross Cutler, Larry Davis

      Pages 155-167

   3. Improvement of On-line Signature Verification System Robust to Intersession Variability

      • Masato Kawamoto, Takayuki Hamamoto, Seiichiro Hangai

      Pages 168-175

8. Classifiers for Recognition

   1. Biometric Identification in Forensic Cases According to the Bayesian Approach

      • J. Gonzalez-Rodriguez, J. Fiérrez-Aguilar, J. Ortega-Garcia, J. J. Lucena-Molina

      Pages 177-185

   2. A New Quadratic Classifier Applied to Biometric Recognition

      • Carlos E. Thomaz, Duncan F. Gillies, Raul Q. Feitosa

      Pages 186-196

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.

• Biometrics
• algorithmic learning
• biometric authentication
• biometric classification
• face recognition
• fingerprint
• fingerprint recognition
• handwriting recognition
• image-based face biometrics
• pattern recognition
• recognition

• Computer Vision Laboratory, DIBEV, University of Sassari, Sassari, Italy

  Massimo Tistarelli

• Halmstad University, Halmstad, Sweden

  Josef Bigun

• Department of Computer Science and Engineering, Michigan State University, East Lansing, USA

  Anil K. Jain

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