Multi-sample data-dependent fusion of sorted score sequences for biometric verification

Ming Cheung Cheung, Man Wai Mak, Sun Yuan Kung

Research output: Journal article publicationConference articleAcademic researchpeer-review

12 Citations (Scopus)


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. In this paper, 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 is applied, the new approach reduces the equal error rate by 11%.
Original languageEnglish
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Publication statusPublished - 27 Sept 2004
EventProceedings - IEEE International Conference on Acoustics, Speech, and Signal Processing - Montreal, Que, Canada
Duration: 17 May 200421 May 2004

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering


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