The Good, the Bad, and the Ugly Face Challenge Problem
Image and Vision Computing
Improving biometric verification systems by fusing Z-norm and F-norm
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Assessing the level of difficulty of fingerprint datasets based on relative quality measures
Information Sciences: an International Journal
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Several studies have shown the existence of biometric zoos. The premise is that in biometric systems people fall into distinct categories, labeled with animal names, indicating recognition difficulty. Different combinations of excessive false accepts or rejects correspond to labels such as: Goat, Lamb, Wolf, etc. Previous work on biometric zoos has investigated the existence of zoos for the results of an algorithm on a data set. This work investigates biometric zoos generalization across algorithms and data sets. For example, if a subject is a Goat for algorithm A on data set X, is that subject also a Goat for algorithm B on data set Y? This paper introduces a theoretical framework for generalizing biometric zoos. Based on our framework, we develop an experimental methodology for determining if biometric zoos generalize across algorithms and data sets, and we conduct a series of experiments to investigate the existence of zoos on two algorithms in FRVT 2006.