Journal ArticleDOI
Likelihood Ratio-Based Biometric Score Fusion
TLDR
Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.Abstract:
Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.read more
Citations
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Proceedings ArticleDOI
Comparison and combination of iris matchers for reliable personal identification
Ajay Kumar,A. Passi +1 more
TL;DR: This paper presents a comparative study of the performance from the iris identification using log-Gabor, Haar wavelet, DCT and FFT based features and suggests the combination of these two matchers is most promising, both in terms of performance and the computational complexity.
Journal ArticleDOI
Multibiometric human recognition using 3D ear and face features
TL;DR: This paper is the first to present feature level fusion of 3D features extracted from ear and frontal face data and scores from L3DF based matching are also fused with iterative closest point algorithm based matching using a weighted sum rule.
Journal ArticleDOI
A Novel Serial Multimodal Biometrics Framework Based on Semisupervised Learning Techniques
TL;DR: The proposed framework addresses the inherent issues of user inconvenience and system inefficiency in parallel multimodal biometric systems by using semisupervised learning techniques to strengthen the matcher on the weaker trait, utilizing the coupling relationship between the weaker and the stronger traits.
Book ChapterDOI
Asymmetry-Based Quality Assessment of Face Images
Guangpeng Zhang,Yunhong Wang +1 more
TL;DR: Three face quality measures are proposed to solve the incapability for performance prediction and remove the requirement for scale normalization of existing methods, using SIFT to extract scale insensitive feature points on face images.
Journal ArticleDOI
Cross-view gait recognition by fusion of multiple transformation consistency measures
TL;DR: A gait recognition algorithm that achieves high accuracy in cases where observation views are different and is formed a hypothesis that the multiple transformed features and original features should be similar to each other if the target subjects are the same.
References
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Journal ArticleDOI
Score normalization in multimodal biometric systems
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