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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Journal ArticleDOI
Deep learning features in facial identification and the likelihood ratio bound.
TL;DR: Zhang et al. as mentioned in this paper investigated deep learning facial features and the score-based likelihood ratio (SLR) levels of their match scores and proposed a new interpretation that the deep learning feature is a class characteristic.
Proceedings Article
Probabilistic Multimodal Classification with dynamic feature selection
TL;DR: Experimental results have shown that the PMC-D methodology offers classification advantages when compared to well-known classification techniques.
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Multi-sensor Image Stitching and Fusion Based Air Infrared Target Cooperative Detection
TL;DR: A novel cooperative detection method of multiple infrared sensors for air small target that can improve the detection rate and reduce the false alarm rate effectively on the condition of limited detection range of each single sensor is proposed.
Dependency modeling for information fusion with applications in visual recognition
TL;DR: This thesis addresses the independent assumption issue in the fusion process and proposes two novel frameworks for dependency modeling and develops an Analytic Dependency Model (ADM) for score level fusion without the assumptions in existing fusion algorithms.
Journal ArticleDOI
Stationary mobile behavioral biometrics: A survey
TL;DR: In this paper , the authors focus on stationary/non-walking (sitting, standing) mobile behavioral biometrics through motion events like acceleration, gyroscope, magnetometer, and orientation (rotation) with the optional support of other non-motion, sporadic modalities such as swipes and keystrokes.
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