Open Science Research Excellence
%0 Journal Article
%A Roman V. Yampolskiy and  Venu Govindaraju
%D 2008 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 24, 2008
%T Similarity Measure Functions for Strategy-Based Biometrics
%U http://waset.org/publications/10863
%V 24
%X Functioning of a biometric system in large part
depends on the performance of the similarity measure function.
Frequently a generalized similarity distance measure function such as
Euclidian distance or Mahalanobis distance is applied to the task of
matching biometric feature vectors. However, often accuracy of a
biometric system can be greatly improved by designing a customized
matching algorithm optimized for a particular biometric application.
In this paper we propose a tailored similarity measure function for
behavioral biometric systems based on the expert knowledge of the
feature level data in the domain. We compare performance of a
proposed matching algorithm to that of other well known similarity
distance functions and demonstrate its superiority with respect to the
chosen domain.
%P 4254 - 4259