Classification and fusion methods for multimodal biometric authentication.

Abstract

Ouyang, Hua.Thesis (M.Phil.)--Chinese University of Hong Kong, 2007.Includes bibliographical references (leaves 81-89).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Biometric Authentication --- p.1Chapter 1.2 --- Multimodal Biometric Authentication --- p.2Chapter 1.2.1 --- Combination of Different Biometric Traits --- p.3Chapter 1.2.2 --- Multimodal Fusion --- p.5Chapter 1.3 --- Audio-Visual Bi-modal Authentication --- p.6Chapter 1.4 --- Focus of This Research --- p.7Chapter 1.5 --- Organization of This Thesis --- p.8Chapter 2 --- Audio-Visual Bi-modal Authentication --- p.10Chapter 2.1 --- Audio-visual Authentication System --- p.10Chapter 2.1.1 --- Why Audio and Mouth? --- p.10Chapter 2.1.2 --- System Overview --- p.11Chapter 2.2 --- XM2VTS Database --- p.12Chapter 2.3 --- Visual Feature Extraction --- p.14Chapter 2.3.1 --- Locating the Mouth --- p.14Chapter 2.3.2 --- Averaged Mouth Images --- p.17Chapter 2.3.3 --- Averaged Optical Flow Images --- p.21Chapter 2.4 --- Audio Features --- p.23Chapter 2.5 --- Video Stream Classification --- p.23Chapter 2.6 --- Audio Stream Classification --- p.25Chapter 2.7 --- Simple Fusion --- p.26Chapter 3 --- Weighted Sum Rules for Multi-modal Fusion --- p.27Chapter 3.1 --- Measurement-Level Fusion --- p.27Chapter 3.2 --- Product Rule and Sum Rule --- p.28Chapter 3.2.1 --- Product Rule --- p.28Chapter 3.2.2 --- Naive Sum Rule (NS) --- p.29Chapter 3.2.3 --- Linear Weighted Sum Rule (WS) --- p.30Chapter 3.3 --- Optimal Weights Selection for WS --- p.31Chapter 3.3.1 --- Independent Case --- p.31Chapter 3.3.2 --- Identical Case --- p.33Chapter 3.4 --- Confidence Measure Based Fusion Weights --- p.35Chapter 4 --- Regularized k-Nearest Neighbor Classifier --- p.39Chapter 4.1 --- Motivations --- p.39Chapter 4.1.1 --- Conventional k-NN Classifier --- p.39Chapter 4.1.2 --- Bayesian Formulation of kNN --- p.40Chapter 4.1.3 --- Pitfalls and Drawbacks of kNN Classifiers --- p.41Chapter 4.1.4 --- Metric Learning Methods --- p.43Chapter 4.2 --- Regularized k-Nearest Neighbor Classifier --- p.46Chapter 4.2.1 --- Metric or Not Metric? --- p.46Chapter 4.2.2 --- Proposed Classifier: RkNN --- p.47Chapter 4.2.3 --- Hyperkernels and Hyper-RKHS --- p.49Chapter 4.2.4 --- Convex Optimization of RkNN --- p.52Chapter 4.2.5 --- Hyper kernel Construction --- p.53Chapter 4.2.6 --- Speeding up RkNN --- p.56Chapter 4.3 --- Experimental Evaluation --- p.57Chapter 4.3.1 --- Synthetic Data Sets --- p.57Chapter 4.3.2 --- Benchmark Data Sets --- p.64Chapter 5 --- Audio-Visual Authentication Experiments --- p.68Chapter 5.1 --- Effectiveness of Visual Features --- p.68Chapter 5.2 --- Performance of Simple Sum Rule --- p.71Chapter 5.3 --- Performances of Individual Modalities --- p.73Chapter 5.4 --- Identification Tasks Using Confidence-based Weighted Sum Rule --- p.74Chapter 5.4.1 --- Effectiveness of WS_M_C Rule --- p.75Chapter 5.4.2 --- WS_M_C v.s. WS_M --- p.76Chapter 5.5 --- Speaker Identification Using RkNN --- p.77Chapter 6 --- Conclusions and Future Work --- p.78Chapter 6.1 --- Conclusions --- p.78Chapter 6.2 --- Important Follow-up Works --- p.80Bibliography --- p.81Chapter A --- Proof of Proposition 3.1 --- p.90Chapter B --- Proof of Proposition 3.2 --- p.9