Adaptive Biometric Systems: Recent Advances and Challenges

ISBN-13: 9783319372228 / Angielski / Miękka / 2016 / 134 str.

Ajita Rattani; Fabio Roli; Eric Granger

Spis treści

Introduction to Adaptive Biometric Systems
Ajita Rattani

Context-Sensitive Self-Updating for Adaptive Face Recognition
C. Pagano, E. Granger, R. Sabourin, P. Tuveri, G.L. Marcialis and F. Roli

Handling Session Mismatch by Semi-Supervised Based Co-Training Scheme
Norman Poh, Joseph Kittler and Ajita Rattani

A Hybrid CRF/HMM for One-Shot Gesture Learning
Selma Belgacem, Clement Chatelain and Thierry Paquet

An Online Learning-Based Adaptive Biometric System
A. Das, R. Kunwar, U. Pal, M. A. Ferrer and M. Blumenstein

Adaptive Facial Recognition Under Aging Effect
Zahid Akhtar, Amr Ahmed, Cigdem Eroglu Erdem and Gian Luca Foresti

An Adaptive Score Level Fusion Scheme for Multimodal Biometric Systems
Kamlesh Tiwari and Phalguni Gupta

O autorze

Dr. Ajita Rattani is a post-doctoral fellow in the Integrated Pattern Recognition and Biometrics (i-PRoBe) lab at Michigan State University, East Lansing, MI, USA. Dr. Fabio Roli is a professor of computer engineering and the Director of the Pattern Recognition and Applications (PRA) lab at the University of Cagliari, Italy. Dr. Eric Granger is a professor in the Department of Automated Manufacturing Engineering and the Director of the Laboratory for Imagery, Vision and Artificial Intelligence at the École de technologie supérieure (ÉTS), Montréal, QC, Canada.

Blurb

This timely and interdisciplinary volume presents a detailed overview of the latest advances and challenges remaining in the field of adaptive biometric systems. A broad range of techniques are provided from an international selection of pre-eminent authorities, collected together under a unified taxonomy and designed to be applicable to any pattern recognition system.

Topics and features:

• Presents a thorough introduction to the concept of adaptive biometric systems, detailing their taxonomy, levels of adaptation, and open issues and challenges
  
• Reviews systems for adaptive face recognition that perform self-updating of facial models using operational (unlabeled) data
  
• Describes a novel semi-supervised training strategy known as fusion-based co-training
  
• Examines the characterization and recognition of human gestures in videos
  
• Discusses a selection of learning techniques that can be applied to build an adaptive biometric system
  
• Investigates procedures for handling temporal variance in facial biometrics due to aging

Proposes a score-level fusion scheme for an adaptive multimodal biometric system

This comprehensive text/reference will be of great interest to researchers and practitioners engaged in systems science, information security or biometrics. Postgraduate and final-year undergraduate

students of computer engineering will also appreciate the coverage of intelligent and adaptive schemes for cutting-edge pattern recognition and signal processing in changing environments.