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A Fusion-Based Approach to Enhancing Multi-Modal Biometric Recognition System Failure Prediction and Overall Performance

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2 Author(s)
Scheirer, W.J. ; VAST Lab., Univ. of Colorado at Colorado Springs, Colorado Springs, CO ; Boult, T.E.

Competing notions of biometric recognition system failure prediction have emerged recently, which can roughly be categorized as quality and non-quality based approaches. Quality, while well correlated overall with recognition performance, is a weaker indication of how the system will perform in a particular instance - something of primary importance for critical installations, screening areas, and surveillance posts. An alternative approach, incorporating a failure prediction receiver operator characteristic (FPROC) analysis has been proposed to overcome the limitations of the quality approach, yielding accurate predictions on a per instance basis. In this paper, we develop a full multi-modal recognition system integrating an FPROC fusion-based failure prediction engine. Four different fusion techniques to enhance failure prediction are developed and evaluated for this system. We present results for the NIST BSSR1 multi-modal data set, and a larger "chimera" set also composed of data from BSSR1. Our results show a significant improvement in recognition performance with the fusion approach, over the baseline recognition results and previous fusion approaches.

Published in:

Biometrics: Theory, Applications and Systems, 2008. BTAS 2008. 2nd IEEE International Conference on

Date of Conference:

Sept. 29 2008-Oct. 1 2008