Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/37986
Title: Multi-sample data-dependent fusion of sorted score sequences for biometric verification
Authors: Cheung, MC
Mak, MW 
Kung, SY
Keywords: Biometrics (access control)
Error statistics
Optimisation
Sensor fusion
Signal sampling
Statistical distributions
Issue Date: 2004
Source: 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing : proceedings : May 17-21, 2004, Fairmont Queen Elizabeth Hotel, Montreal, Quebec, Canada, p. V-681-V684 How to cite?
Abstract: In many biometric systems, the scores of multiple samples (e.g. utterances) are averaged and the average score is compared against a decision threshold for decision making. The average score, however, may not be optimal because the distribution of the scores is ignored. To address this limitation, we have recently proposed a fusion model that incorporates the score distribution by making the fusion weights dependent on the dispersion between the frame-based scores and the prior score statistics obtained from training data. As the fusion weights are data-dependent, the positions of scores in the score sequences become detrimental to the final fused scores. We propose to enhance the fusion model by sorting the score sequences before fusion takes place. The fusion model was evaluated on a speaker verification task where each claimant utters two utterances in a verification session. Results demonstrate that fusion of sorted scores has the effect of maximizing the dispersion between the client scores and the impostor scores, making the verification process more reliable. Compared with our previous work, where no sorting was applied, the new approach reduces the equal error rate by 11 %.
URI: http://hdl.handle.net/10397/37986
ISBN: 0-7803-8484-9
ISSN: 1520-6149
DOI: 10.1109/ICASSP.2004.1327202
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