Multibiometrics for human identification

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 m.

Cover; Multibiometrics for Human Identification; Title; Copyright; Contents; List of Contributors; Preface; Introduction; Part I: Multimodal and Multisensor Biometric Systems; Part II: Fusion Methods in Multibiometric Systems; Part III: Hybrid Biometric Systems; Part IV: Databases and Security; Part V: Performance of Multibiometric Systems; PART I: Multimodal and Multisensor Biometric Systems; 1 Multimodal Ear and Face Modeling and Recognition; 1.1 Introduction; 1.2 Related Work; 1.2.1 3D Ear Recognition; 1.2.2 2D Face Recognition; 1.2.3 Multimodal Ear and Face Recognition.

1.3 3D Ear Modeling and Recognition Using Shape from Shading1.3.1 Linear Shape from Shading; 1.3.2 Similarity Accumulator; 1.3.3 3D Model Selection; 1.3.4 Recognition Process; 1.4 2D Face Recognition Using Gabor Features; 1.5 Data Fusion; 1.6 Experiments and Results; 1.7 Conclusions and Future Work; Appendix; References; 2 Audiovisual Speech Synchrony Detection by a Family of Bimodal Linear Prediction Models; 2.1 Introduction; 2.2 The Mutual Information-Based Approach; 2.3 The Hypothesis Testing-Based Approach; 2.4 Time Evolution-Based Bimodal Linear Prediction.

2.5 Generalized Bimodal Linear Prediction2.6 Au…