An Evaluation of Score Level Fusion Approaches for Fingerprint and Finger-vein Biometrics

Biometric systems have to address many requirements, such as large population coverage, demographic diversity, varied deployment environment, as well as practical aspects like performance and spoofing attacks. Traditional unimodal biometric systems do not fully meet the aforementioned requirements making them vulnerable and susceptible to different types of attacks. In response to that, modern biometric systems combine multiple biometric modalities at different fusion levels. The fused score is decisive to classify an unknown user as a genuine or impostor. In this paper, we evaluate combinations of score normalization and fusion techniques using two modalities (fingerprint and finger-vein) with the goal of identifying which one achieves better improvement rate over traditional unimodal biometric systems. The individual scores obtained from finger-veins and fingerprints are combined at score level using three score normalization techniques (min-max, z-score, hyperbolic tangent) and four score fusion approaches (minimum score, maximum score, simple sum, user weighting). The experimental results proved that the combination of hyperbolic tangent score normalization technique with the simple sum fusion approach achieve the best improvement rate of 99.98%.Comment: 10 pages, 5 figures, 3 tables, conference, NISK 201

Ensuring the identity of a user in time : a multi-modal fuzzy approach

This work proposes a fuzzy multimodal technique capable of guaranteeing the desired level of security while keeping under control the high costs typically associated to some biometric authentication devices. Specifically we describe a fuzzy controller choosing within a palette of authentication techniques to continuously check and confirm its trust in the identity of a user

Toward trust-based multi-modal user authentication on the Web : a fuzzy approach

In the last few years authentication has become of paramount importance both on the corporate Intranets and on the global Web. While most approaches focus on the initial authentication and then no further check ensure the identity of the navigating user, in this work we present a fuzzy approach to multi-modal authentication for a trust-based, continuous identity check during Web navigation. The potentiality of such an approach for generating trust-based metadata is also discussed

Model-based 3d gait biometric using quadruple fusion classifier

The area of gait biometrics has received significant interest in the last few years, largely due to the unique suitability and reliability of gait pattern as a human recognition technique. The advantage of gait over other biometrics is that it can perform non-intrusive data acquisition and can be captured from a distance. Current gait analysis approach can be divided into model-free and model-based approach. The gait data extracted for identification process may be influenced by ambient noise conditions, occlusion, changes in backgrounds and illumination when model-free 2D silhouette data is considered. In addition, the performance in gait biometric recognition is often affected by covariate factors such as walking condition and footwear. These are often related to low performance of personal verification and identification. While body biometrics constituted of both static and dynamics features of gait motion, they can complement one another when used jointly to maximise recognition performance. Therefore, this research proposes a model-based technique that can overcome the above limitations. The proposed technique covers the process of extracting a set of 3D static and dynamic gait features from the 3D skeleton data in different covariate factors such as different footwear and walking condition. A skeleton model from forty subjects was acquired using Kinect which was able to provide human skeleton and 3D joints and the features were extracted and categorized into static and dynamic data. Normalization process was performed to scale down the features into a specific range of structure, followed by feature selection process to select the most significant features to be used in classification. The features were classified separately using five classification algorithms for static and dynamic features. A new fusion framework is proposed based on score level fusion called Quadruple Fusion Framework (QFF) in order to combine the static and dynamic features obtained from the classification model. The experimental result of static and dynamic fusion achieved the accuracy of 99.5% for footwear covariates and 97% for walking condition covariates. The result of the experimental validation demonstrated the viability of gait as biometrics in human recognition

Classification and fusion methods for multimodal biometric authentication.

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

Decision fusion for multi-modal person authentication.

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.1Chapter 1.1. --- Objectives --- p.4Chapter 1.2. --- Thesis Outline --- p.5Chapter 2. --- Background --- p.6Chapter 2.1. --- User Authentication Systems --- p.6Chapter 2.2. --- Biometric Authentication --- p.9Chapter 2.2.1. --- Speaker Verification System --- p.9Chapter 2.2.2. --- Face Verification System --- p.10Chapter 2.2.3. --- Fingerprint Verification System --- p.11Chapter 2.3. --- Verbal Information Verification (VIV) --- p.12Chapter 2.4. --- Combining SV and VIV --- p.15Chapter 2.5. --- Biometric Decision Fusion Techniques --- p.17Chapter 2.6. --- Fuzzy Logic --- p.20Chapter 2.6.1. --- Fuzzy Membership Function and Fuzzy Set --- p.21Chapter 2.6.2. --- Fuzzy Operators --- p.22Chapter 2.6.3. --- Fuzzy Rules --- p.22Chapter 2.6.4. --- Defuzzification --- p.23Chapter 2.6.5. --- Advantage of Using Fuzzy Logic in Biometric Fusion --- p.23Chapter 2.7. --- Chapter Summary --- p.25Chapter 3. --- Experimental Data --- p.26Chapter 3.1. --- Data for Multi-biometric Fusion --- p.26Chapter 3.1.1. --- Speech Utterances --- p.30Chapter 3.1.2. --- Face Movement Video Frames --- p.31Chapter 3.1.3. --- Fingerprint Images --- p.32Chapter 3.2. --- Data for Speech Authentication Fusion --- p.33Chapter 3.2.1. --- SV Training Data for Speaker Model --- p.34Chapter 3.2.2. --- VIV Training Data for Speaker Independent Model --- p.34Chapter 3.2.3. --- Validation Data --- p.34Chapter 3.3. --- Chapter Summary --- p.36Chapter 4. --- Authentication Modules --- p.37Chapter 4.1. --- Biometric Authentication --- p.38Chapter 4.1.1. --- Speaker Verification --- p.38Chapter 4.1.2. --- Face Verification --- p.38Chapter 4.1.3. --- Fingerprint Verification --- p.39Chapter 4.1.4. --- Individual Biometric Performance --- p.39Chapter 4.2. --- Verbal Information Verification (VIV) --- p.42Chapter 4.3. --- Chapter Summary --- p.44Chapter 5. --- Weighted Average Fusion for Multi-Modal Biometrics --- p.46Chapter 5.1. --- Experimental Setup and Results --- p.46Chapter 5.2. --- Analysis of Weighted Average Fusion Results --- p.48Chapter 5.3. --- Chapter Summary --- p.59Chapter 6. --- Fully Adaptive Fuzzy Logic Decision Fusion Framework --- p.61Chapter 6.1. --- Factors Considered in the Estimation of Biometric Sample Quality --- p.62Chapter 6.1.1. --- Factors for Speech --- p.63Chapter 6.1.2. --- Factors for Face --- p.65Chapter 6.1.3. --- Factors for Fingerprint --- p.70Chapter 6.2. --- Fuzzy Logic Decision Fusion Framework --- p.76Chapter 6.2.1. --- Speech Fuzzy Sets --- p.77Chapter 6.2.2. --- Face Fuzzy Sets --- p.79Chapter 6.2.3. --- Fingerprint Fuzzy Sets --- p.80Chapter 6.2.4. --- Output Fuzzy Sets --- p.81Chapter 6.2.5. --- Fuzzy Rules and Other Information --- p.83Chapter 6.3. --- Experimental Setup and Results --- p.84Chapter 6.4. --- Comparison Between Weighted Average and Fuzzy Logic Decision Fusion --- p.86Chapter 6.5. --- Chapter Summary --- p.95Chapter 7. --- Factors Affecting VIV Performance --- p.97Chapter 7.1. --- Factors from Verbal Messages --- p.99Chapter 7.1.1. --- Number of Distinct-Unique Responses --- p.99Chapter 7.1.2. --- Distribution of Distinct-Unique Responses --- p.101Chapter 7.1.3. --- Inter-person Lexical Choice Variations --- p.103Chapter 7.1.4. --- Intra-person Lexical Choice Variations --- p.106Chapter 7.2. --- Factors from Utterance Verification --- p.108Chapter 7.2.1. --- Thresholding --- p.109Chapter 7.2.2. --- Background Noise --- p.113Chapter 7.3. --- VIV Weight Estimation Using PDP --- p.115Chapter 7.4. --- Chapter Summary --- p.119Chapter 8. --- Adaptive Fusion for SV and VIV --- p.121Chapter 8.1. --- Weighted Average fusion of SV and VIV --- p.122Chapter 8.1.1. --- Scores Normalization --- p.123Chapter 8.1.2. --- Experimental Setup --- p.123Chapter 8.2. --- Adaptive Fusion for SV and VIV --- p.124Chapter 8.2.1. --- Components of Adaptive Fusion --- p.126Chapter 8.2.2. --- Three Categories Design --- p.129Chapter 8.2.3. --- Fusion Strategy for Each Category --- p.132Chapter 8.2.4. --- SV Driven Approach --- p.133Chapter 8.3. --- SV and Fixed-Pass Phrase VIV Fusion Results --- p.133Chapter 8.4. --- SV and Key-Pass Phrase VIV Fusion Results --- p.136Chapter 8.5. --- Chapter Summary --- p.141Chapter 9. --- Conclusions and Future Work --- p.143Chapter 9.1. --- Conclusions --- p.143Chapter 9.2. --- Future Work --- p.145Bibliography --- p.147Appendix A Detail of BSC Speech --- p.153Appendix B Fuzzy Rules for Multimodal Biometric Fusion --- p.155Appendix C Full Example for Multimodal Biometrics Fusion --- p.157Appendix DReason for Having a Flat Error Surface --- p.161Appendix E Reason for Having a Relative Peak Point in the Middle of the Error Surface --- p.164Appendix F Illustration on Fuzzy Logic Weight Estimation --- p.166Appendix GExamples for SV and Key-Pass Phrase VIV Fusion --- p.17