M. Tech. Computer System Engineering / A single biometric trait for authentication is widely used in some application areas where security is of high importance. However, biometric systems are susceptible to noise, intraclass variation, non-universality and spoof attacks. Thus, there is need to use algorithms that overcome all these limitations found in biometric systems. The use of multimodal biometrics can improve the performance of authentication system. This study proposed using both fingerprint and face for authentication in access system.

published_or_final_version / Medical Sciences / Master / Master of Medical Sciences

This study examines the emotional responses of adoption reunion participants over time by a cross-sectional survey. A questionnaire was modeled after an existing reunion guideline, "Relationship Stages in Reunion". Ontario adoption reunion counselors in the public and private sectors use this guideline. This questionnaire was circulated by a Parent Finder's organization (22 respondents), and a provincial government organization (27 respondents). The hypothesis that there is a predictable pattern of emotional response in reunion was not supported. No significant differences were found between those that experienced reunion via either organization. There were some differences in the demographics. Both groups noted a high level of satisfaction with their reunion. This research would have been better tracked by a longitudinal study.

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Abstract Cold-tolerant bacteria, also known as psychrotrophic bacteria, are notorious contaminants of milk in the refrigerated dairy food chain. These organisms, especially the pseudomonads, may produce heatresistant enzymes that are responsible for the breakdown of proteins and lipids in milk and dairy products. Such reactions result in a variety of defects in the raw or unprocessed milk that may affect the suitability of such milk for further processing. The enzymes produced may cause defects in long-life dairy products such as cheese, butter and long-life milk. In the present study, a range of 18 yellow pigmented psychrotrophic bacteria, collectively known as flavobacteria, were isolated from local dairy products. One aim of this study was to identify these bacteria to species level using molecular techniques. A second aim was to determine the spoilage potential of these organisms based on profiles generated by the BIOLOG system (that may relate to hydrolytic enzymes produced). Of the 18 isolates, 14 belonged to the genus Chryseobacterium while 4 were identified as Empedobacter isolates. The most active spoilage organisms in this group were shown to be C. bovis, C. shigense and E. brevis. These findings illustrate that enzymatically catalysed defects in dairy products should not be attributed solely to acknowledged psychrotrophic bacteria such as the pseudomonads, but that flavobacterial species may also be actively involved.

La biométrie, entendue comme un ensemble de techniques produisant une information à partir d’une mesure corporelle (empreintes digitales, génétiques, photographies anthropométriques) afin de la comparer avec une donnée préenregistrée, n’a pas encore fait l’objet d’une étude juridique. Ainsi, à partir des fondements de l’utilisation de la biométrie, à savoir l’étude des notions juridiques de corps et d’identité, cette thèse a porté sur les fonctions déclarées ou latentes de la biométrie pour en montrer la pluralité et en singulariser la continuité.En se fondant sur l’utilisation de la biométrie en tant que mode de preuve de l’identité de la personne physique, il a été possible de retracer une évolution des fonctions, partant de la fonction identificatoire des fichiers de police pour tendre vers une identification en temps réel. Cette finalité a pour corollaire une autre fonction latente, mais structurelle à l’emploi des systèmes biométriques : la localisation des personnes physiques.Cette fonction irrigue l’ensemble des utilisations des systèmes biométriques, sans pour autant être précisée, en tant que telle, par le droit positif. Pourtant, son existence modifie profondément la conciliation opérée entre la défense de l’ordre public et la protection des droits fondamentaux, au détriment de ces derniers. Dès lors, la biométrie, en tant que concept, connaît un encadrement juridique lacunaire qui ne permet pas une protection effective des droits fondamentaux.En effet, les droits fondamentaux classiquement mobilisés pour limiter les traitements de données à caractère personnel, au premier rang desquels la vie privée, ne saisissent que marginalement les spécificités de la biométrie. Car en l’état actuel du droit positif, il n’existe actuellement aucun droit à l’anonymat, notamment sur l’espace public, qui pourrait encadrer cette fonction latente de la biométrie,à savoir la localisation des personnes physiques. / Biometrics, known as a set of techniques to produce information from a bodypart able to compare it with a pre-recorded data has not yet been the subject of a legal study. Thus, from the foundations of the use of biometrics, ie the study of the legal concepts of body and identity, this work focused on the declared or latent functions of biometrics in order to show the plurality and singling the continuity. Based on the use of biometrics as a means of proof of identity of the individual, it was possible to trace an evolution of functions, starting from the identification function of the police files to strive for identification in real time. This last function corollary another latent function, but structural to the use of biometric systems: the location of individuals. This function irrigates all users of biometric systems, without being specified as such by law. Yet his life profoundly changes the balance between the defense of public order and the protection of fundamental rights, to the detriment of the latter. Therefore, biometrics, as a concept, has a deficient legal framework that does not allow effective protection of fundamental rights. Because there is currently no right to anonymity in particular on public space that could frame the single latent function of biometrics: the localisation of individuals.

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

Lau, Chun Wai. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 119-123). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Multimodal Biometric Systems --- p.3 / Chapter 1.3 --- Objectives --- p.7 / Chapter 1.4 --- Thesis Outline --- p.7 / Chapter 2 --- Background --- p.9 / Chapter 2.1 --- Decision Fusions in Multimodal Biometric Systems --- p.10 / Chapter 2.2 --- Fuzzy Logic --- p.15 / Chapter 2.2.1 --- Fuzzy Sets and Their Operations --- p.15 / Chapter 2.2.2 --- Fuzzy Rules --- p.17 / Chapter 2.2.3 --- Defuzzification --- p.18 / Chapter 2.2.4 --- Applications of Fuzzy Logic --- p.19 / Chapter 2.3 --- Demspter-Shafer Theory of Evidence --- p.20 / Chapter 2.3.1 --- Belief and Plausibility --- p.20 / Chapter 2.3.2 --- Dempster's Rule of Combination --- p.21 / Chapter 2.3.3 --- Applications of Dempster-Shafer Theory of Evidence --- p.22 / Chapter 2.4 --- Chapter Summary --- p.23 / Chapter 3 --- Biometric Modalities --- p.24 / Chapter 3.1 --- Speaker Verification --- p.24 / Chapter 3.1.1 --- Data Collection --- p.25 / Chapter 3.1.2 --- Experiment and Results --- p.26 / Chapter 3.2 --- Face Identification --- p.27 / Chapter 3.2.1 --- Data Collection --- p.28 / Chapter 3.2.2 --- Experiment and Results --- p.29 / Chapter 3.3 --- Fingerprint Verification --- p.35 / Chapter 3.3.1 --- Data Collection --- p.36 / Chapter 3.3.2 --- Experiment and Results --- p.37 / Chapter 3.4 --- Chapter Summary --- p.38 / Chapter 4 --- Baseline Fusions --- p.39 / Chapter 4.1 --- Majority Voting --- p.40 / Chapter 4.2 --- Fusion by Weighted Average Scores --- p.45 / Chapter 4.3 --- Comparison of Fusion by Majority Voting and Fusion by Weighted Average Scores --- p.51 / Chapter 4.4 --- Chapter Summary --- p.53 / Chapter 5 --- Fuzzy Logic Decision Fusion --- p.54 / Chapter 5.1 --- Introduction --- p.55 / Chapter 5.2 --- Fuzzy Inference System --- p.56 / Chapter 5.2.1 --- Input Fuzzy Variables and Fuzzy Sets for Face Biometric --- p.56 / Chapter 5.2.2 --- Input Fuzzy Variables and Fuzzy Sets for Fingerprint Biometric --- p.59 / Chapter 5.2.3 --- Output Fuzzy Variables and Fuzzy Sets --- p.62 / Chapter 5.2.4 --- Fuzzy Rules for Face Biometric --- p.63 / Chapter 5.2.5 --- Fuzzy Rules for Fingerprint Biometric --- p.64 / Chapter 5.3 --- Experiments with Fuzzy Logic Fusion --- p.66 / Chapter 5.4 --- Significance Testing --- p.71 / Chapter 5.5 --- Comparison of Fuzzy Logic Fusion and Weighted Average Scores --- p.74 / Chapter 5.6 --- Testing of Fuzzy Rule Properties --- p.76 / Chapter 5.6.1 --- Experiment 1 --- p.77 / Chapter 5.6.2 --- Experiment 2 --- p.80 / Chapter 5.6.3 --- Experiment 3 --- p.83 / Chapter 5.6.4 --- Comparison of Results --- p.86 / Chapter 5.7 --- Chapter Summary --- p.86 / Chapter 6 --- Decision Fusion Based on Dempster-Shafer Theory of Evi- dence --- p.88 / Chapter 6.1 --- Introduction --- p.89 / Chapter 6.2 --- Framework of Fusion Based on Dempster-Shafer Theory of Evidence --- p.90 / Chapter 6.2.1 --- Evidences for Biometric Systems --- p.91 / Chapter 6.2.2 --- Intra-Modality Combination --- p.95 / Chapter 6.2.3 --- Inter-Modality Combination --- p.97 / Chapter 6.3 --- Experiments with Fusion Based on Dempster-Shafer Theory of Evidence --- p.99 / Chapter 6.4 --- Significance Testing --- p.103 / Chapter 6.5 --- Comparison of Fusion Based on Dempster-Shafer Theory of Evidence and Weighted Average Scores --- p.106 / Chapter 6.6 --- Comparison of Fusion Based on Dempster-Shafer Theory of Evidence and Fuzzy Logic Fusion --- p.108 / Chapter 6.7 --- Chapter Summary --- p.110 / Chapter 7 --- Conclusions --- p.112 / Chapter 7.1 --- Summary --- p.112 / Chapter 7.2 --- Contributions --- p.115 / Chapter 7.3 --- Future Work --- p.117 / Bibliography --- p.119 / Chapter A --- Fuzzy Rules --- p.124