Utilizing Independence of Multimodal Biometric Matchers

Abstract

The problem of combining biometric matchers for person verification can be viewed as a pattern classification problem, and any trainable pattern classification algorithm can be used for score combination. But biometric matchers of different modalities possess a property of the statistical independence of their output scores. In this work we investigate if utilizing this independence knowledge results in the improvement of the combination algorithm. We show both theoretically and experimentally that utilizing independence provides better approximation of score density functions, and results in combination improvement.