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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010270381 | TK7882.B56 M86 2011 | Open Access Book | Book | Searching... |
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Summary
Summary
In today's security-conscious society, real-world applications for authentication or identification require a highly accurate system for recognizing individual humans. The required level of performance cannot be achieved through the use of a single biometric such as face, fingerprint, ear, iris, palm, gait, or speech. Fusing multiple biometrics enables the indexing of large databases, more robust performance, and enhanced coverage of populations. Multiple biometrics are also naturally more robust against attacks than single biometrics. This book addresses a broad spectrum of research issues on multibiometrics for human identification, ranging from sensing modes and modalities to fusion of biometric samples and combination of algorithms. It covers publicly available multibiometrics databases, theoretical and empirical studies on sensor fusion techniques in the context of biometrics authentication, identification, and performance evaluation and prediction.
Author Notes
Dr. Bir Bhanu is the Distinguished Professor of Electrical Engineering and serves as the Director of the Interdisciplinary Center for Research in Intelligent Systems and the Visualization and Intelligent Systems Laboratory at the University of California, Riverside (UCR). He is a coauthor of seven authored books and three edited books, has 12 patents, and has authored more than 350 reviewed technical publications, including more than 100 journal papers.
Dr. Venu Govindaraju is a UB Distinguished Professor of Computer Science and Engineering at the University at Buffalo (SUNY Buffalo) and the founder of the Center for Unified Biometrics and Sensors (CUBS). He has coauthored more than 300 reviewed technical papers, four U.S. patents, and two books.
Table of Contents
List of Contributors | p. vii |
Preface | p. xii |
Introduction | p. 1 |
Part I Multimodal and Multisensor Biometric Systems | |
1 Multimodal Ear and Face Modeling and Recognition | p. 9 |
2 Audiovisual Speech Synchrony Detection by a Family of Bimodal Linear Prediction Models | p. 31 |
3 Multispectral Contact-Free Palmprint Recognition | p. 51 |
4 Face Recognition under the Skin | p. 74 |
Part II Fusion Methods in Multibiometric Systems | |
5 Biometric Authentication: A Copula-Based Approach | p. 95 |
6 An Investigation into Feature-Level Fusion of Face and Fingerprint Biometrics | p. 120 |
7 Adaptive Multibiometric Systems | p. 143 |
Part III Hybrid Biometric Systems | |
8 Multiple Projector Camera System for Three-Dimensional Gait Recognition | p. 173 |
9 Gait Recognition Using Motion Physics in a Neuromorphic Computing Framework | p. 206 |
10 Face Tracking and Recognition in a Camera Network | p. 235 |
11 Bidirectional Relighting for 3D-Aided 2D Face Recognition | p. 258 |
Part IV Databases and Security | |
12 Acquisition and Analysis of a Dataset Comprising Gait, Ear, and Semantic Data | p. 277 |
13 Dynamic Security Management in Multibiometrics | p. 302 |
Part V Performance of Multibiometric Systems | |
14 Prediction for Fusion of Biometrics Systems | p. 323 |
15 Predicting Performance in Large-Scale Identification Systems by Score Resampling | p. 363 |