Dissimilarity functions for behavior-based biometrics
Quality of a biometric system is directly related to the performance of the dissimilarity measure function. Frequently a generalized dissimilarity measure function such as Mahalanobis distance is applied to the task of matching biometric feature vectors. However, often accuracy of a biometric system can be greatly improved by introducing a customized matching algorithm optimized for a particular biometric. In this paper we investigate two tailored similarity measure functions for behavioral biometric systems based on the expert knowledge of the data in the domain. We compare performance of proposed matching algorithms to that of other well known similarity distance functions and demonstrate superiority of one of the new algorithms with respect to the chosen domain.