Abstract
A methodology for fusing multiple instances of biometric data to improve the performance of a personal identity verification system is developed. The fusion problem is formulated in the framework of the Bayesian estimation theory. The effect of different fusion strategies on the error probability is analysed theoretically. The proposed methodology is then demonstrated on the problem of personal identity verification using multiple facial images. Experimental studies on the M2VTS database confirm the predicted improvements in performance. A reduction in error rates of up to 40% is achieved. The performance gains are initially monotonic but they tend to saturate after integrating the first few observations. It is also shown that the fusion based on rank order statistic, i.e., the median, is robust to outliers.
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