BMDT: An optimized method for Biometric Menagerie Detection

Abstract

Biometric menagerie is an important phenomenon in biometric systems, which focuses on distinguishing the minority of people who perform poorly and cause the majority of the errors (FAR and FRR). It can help to evaluate biometric systems and improve their performance by analyzing the animal like users. The fundamental step of this theory is the detection of animals. If the detection is not accurate, it may lead to potential problems. However, the current theories carried out by Doddington et al. (1998) and Yager (2008) both neglected the threshold in biometric systems when detecting animals, which might reduce the accuracy of animal detection. To verify this conjecture, we apply the above two theories to detect the existence of animals on a special finger vein database PFVD - Perfect Finger Vein Database. The characteristic of PFVD is that its accuracy is 100%, indicating zero FAR and zero FRR. From the intuitive point of view, there should exist no goat, lamb or wolf in Doddington's menagerie, and no worm, chameleon or phantom in Yager's menagerie. However, the experiments show the negative results, implying that the current theories are not perfect on animal detection. This paper analyzes the two theories and proposes BMDT - Biometric Menagerie Detection with Threshold, an optimized method based on Yager. By taking threshold into account, BMDT makes a significant improvement on the accuracy of animal detection, compared to the current theory. We apply BMDT on PFVD, and the results show that the falsely detected animals by Yager's method are removed. In addition, we evaluate BMDT in 3 more general cases, proving the advantage of the proposed method.