Open Science Research Excellence
@article{(International Science Index):,
  title    = {Similarity Measure Functions for Strategy-Based Biometrics},
  author    = {Roman V. Yampolskiy and  Venu Govindaraju},
  country   = {},
  abstract  = {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.},
    journal   = {International Journal of Computer, Electrical, Automation, Control and Information Engineering},  volume    = {2},
  number    = {12},
  year      = {2008},
  pages     = {4254 - 4259},
  ee        = {},
  url       = {},
  bibsource = {},
  issn      = {eISSN:1307-6892},
  publisher = {World Academy of Science, Engineering and Technology},
  index     = {International Science Index 24, 2008},