On using error bounds to optimize cost-sensitive multimodal biometric authentication

While using more biometric traits in multimodal biometric fusion can effectively increase the system robustness, often, the cost associated to adding additional systems is not considered. In this paper, we propose an algorithm that can efficiently bound the biometric system error. This helps not only to speed up the search for the optimal system configuration by an order of magnitude but also unexpectedly to enhance the robustness to population mismatch. This suggests that bounding the error of biometric system from above can possibly be better than directly estimating it from the data. The latter strategy can be susceptible to spurious biometric samples and the particular choice of users. The efficiency of the proposal is achieved thanks to the use of Chernoff bound in estimating the authentication error. Unfortunately, such a bound assumes that the match scores are normally distributed, which is not necessarily the correct distribution model. We propose to transform simultaneously the class conditional match scores (genuine user or impostor scores) into ones that are more conforming to normal distributions using a modified criterion of the Box-Cox transform.