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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An improvement to linear regression classification for face recognition
TL;DR: Experimental results on the ORL, FERET, Libor94 and CMU-PIE face databases demonstrate that the proposed LRC based method obtains a higher recognition rate than some state-of-the-art face recognition methods.
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Integrating Sparse and Collaborative Representation Classifications for Image Classification
TL;DR: Collaborative representation classification (CRC) is an important sparse method, which is easy to carry out and uses a linear combination of training samples to represent a test sample.
Proceedings ArticleDOI
Multi-modal biometric authentication fusing iris and palmprint based on GMM
TL;DR: A new fusion scheme at score level that combines Gaussian mixture model (GMM) and score normalization is proposed and can dramatically improve the system performance.
Journal ArticleDOI
Compressive Sensing-Based Detection With Multimodal Dependent Data
TL;DR: A Gaussian approximation is employed to perform likelihood ratio (LR) based detection with compressed data, and a nonparametric approach where a decision statistic based on the second order statistics of uncompressed data is computed in the compressed domain is developed.
Journal ArticleDOI
Likelihood ratio based features for a trained biometric score fusion
TL;DR: Several tests on different biometric verification systems show that the new method outperforms other trained and non-trained approaches for combining biometric matchers at the score level.
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