Hui Pak Sum Henry. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (leaves [147]-152). / Abstracts in English and Chinese. / Chapter 1. --- Introduction --- p.1 / Chapter 1.1. --- Objectives --- p.4 / Chapter 1.2. --- Thesis Outline --- p.5 / Chapter 2. --- Background --- p.6 / Chapter 2.1. --- User Authentication Systems --- p.6 / Chapter 2.2. --- Biometric Authentication --- p.9 / Chapter 2.2.1. --- Speaker Verification System --- p.9 / Chapter 2.2.2. --- Face Verification System --- p.10 / Chapter 2.2.3. --- Fingerprint Verification System --- p.11 / Chapter 2.3. --- Verbal Information Verification (VIV) --- p.12 / Chapter 2.4. --- Combining SV and VIV --- p.15 / Chapter 2.5. --- Biometric Decision Fusion Techniques --- p.17 / Chapter 2.6. --- Fuzzy Logic --- p.20 / Chapter 2.6.1. --- Fuzzy Membership Function and Fuzzy Set --- p.21 / Chapter 2.6.2. --- Fuzzy Operators --- p.22 / Chapter 2.6.3. --- Fuzzy Rules --- p.22 / Chapter 2.6.4. --- Defuzzification --- p.23 / Chapter 2.6.5. --- Advantage of Using Fuzzy Logic in Biometric Fusion --- p.23 / Chapter 2.7. --- Chapter Summary --- p.25 / Chapter 3. --- Experimental Data --- p.26 / Chapter 3.1. --- Data for Multi-biometric Fusion --- p.26 / Chapter 3.1.1. --- Speech Utterances --- p.30 / Chapter 3.1.2. --- Face Movement Video Frames --- p.31 / Chapter 3.1.3. --- Fingerprint Images --- p.32 / Chapter 3.2. --- Data for Speech Authentication Fusion --- p.33 / Chapter 3.2.1. --- SV Training Data for Speaker Model --- p.34 / Chapter 3.2.2. --- VIV Training Data for Speaker Independent Model --- p.34 / Chapter 3.2.3. --- Validation Data --- p.34 / Chapter 3.3. --- Chapter Summary --- p.36 / Chapter 4. --- Authentication Modules --- p.37 / Chapter 4.1. --- Biometric Authentication --- p.38 / Chapter 4.1.1. --- Speaker Verification --- p.38 / Chapter 4.1.2. --- Face Verification --- p.38 / Chapter 4.1.3. --- Fingerprint Verification --- p.39 / Chapter 4.1.4. --- Individual Biometric Performance --- p.39 / Chapter 4.2. --- Verbal Information Verification (VIV) --- p.42 / Chapter 4.3. --- Chapter Summary --- p.44 / Chapter 5. --- Weighted Average Fusion for Multi-Modal Biometrics --- p.46 / Chapter 5.1. --- Experimental Setup and Results --- p.46 / Chapter 5.2. --- Analysis of Weighted Average Fusion Results --- p.48 / Chapter 5.3. --- Chapter Summary --- p.59 / Chapter 6. --- Fully Adaptive Fuzzy Logic Decision Fusion Framework --- p.61 / Chapter 6.1. --- Factors Considered in the Estimation of Biometric Sample Quality --- p.62 / Chapter 6.1.1. --- Factors for Speech --- p.63 / Chapter 6.1.2. --- Factors for Face --- p.65 / Chapter 6.1.3. --- Factors for Fingerprint --- p.70 / Chapter 6.2. --- Fuzzy Logic Decision Fusion Framework --- p.76 / Chapter 6.2.1. --- Speech Fuzzy Sets --- p.77 / Chapter 6.2.2. --- Face Fuzzy Sets --- p.79 / Chapter 6.2.3. --- Fingerprint Fuzzy Sets --- p.80 / Chapter 6.2.4. --- Output Fuzzy Sets --- p.81 / Chapter 6.2.5. --- Fuzzy Rules and Other Information --- p.83 / Chapter 6.3. --- Experimental Setup and Results --- p.84 / Chapter 6.4. --- Comparison Between Weighted Average and Fuzzy Logic Decision Fusion --- p.86 / Chapter 6.5. --- Chapter Summary --- p.95 / Chapter 7. --- Factors Affecting VIV Performance --- p.97 / Chapter 7.1. --- Factors from Verbal Messages --- p.99 / Chapter 7.1.1. --- Number of Distinct-Unique Responses --- p.99 / Chapter 7.1.2. --- Distribution of Distinct-Unique Responses --- p.101 / Chapter 7.1.3. --- Inter-person Lexical Choice Variations --- p.103 / Chapter 7.1.4. --- Intra-person Lexical Choice Variations --- p.106 / Chapter 7.2. --- Factors from Utterance Verification --- p.108 / Chapter 7.2.1. --- Thresholding --- p.109 / Chapter 7.2.2. --- Background Noise --- p.113 / Chapter 7.3. --- VIV Weight Estimation Using PDP --- p.115 / Chapter 7.4. --- Chapter Summary --- p.119 / Chapter 8. --- Adaptive Fusion for SV and VIV --- p.121 / Chapter 8.1. --- Weighted Average fusion of SV and VIV --- p.122 / Chapter 8.1.1. --- Scores Normalization --- p.123 / Chapter 8.1.2. --- Experimental Setup --- p.123 / Chapter 8.2. --- Adaptive Fusion for SV and VIV --- p.124 / Chapter 8.2.1. --- Components of Adaptive Fusion --- p.126 / Chapter 8.2.2. --- Three Categories Design --- p.129 / Chapter 8.2.3. --- Fusion Strategy for Each Category --- p.132 / Chapter 8.2.4. --- SV Driven Approach --- p.133 / Chapter 8.3. --- SV and Fixed-Pass Phrase VIV Fusion Results --- p.133 / Chapter 8.4. --- SV and Key-Pass Phrase VIV Fusion Results --- p.136 / Chapter 8.5. --- Chapter Summary --- p.141 / Chapter 9. --- Conclusions and Future Work --- p.143 / Chapter 9.1. --- Conclusions --- p.143 / Chapter 9.2. --- Future Work --- p.145 / Bibliography --- p.147 / Appendix A Detail of BSC Speech --- p.153 / Appendix B Fuzzy Rules for Multimodal Biometric Fusion --- p.155 / Appendix C Full Example for Multimodal Biometrics Fusion --- p.157 / Appendix DReason for Having a Flat Error Surface --- p.161 / Appendix E Reason for Having a Relative Peak Point in the Middle of the Error Surface --- p.164 / Appendix F Illustration on Fuzzy Logic Weight Estimation --- p.166 / Appendix GExamples for SV and Key-Pass Phrase VIV Fusion --- p.175
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_325651 |
| Date | January 2006 |
| Contributors | Hui, Pak Sum Henry., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xviii, 176 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
Page generated in 0.0022 seconds