Fast and accurate biometric identification using score level indexing and fusion

Biometric identification provides a very convenient way to authenticate a user because it does not require the user to claim an identity. However, both the identification error rates and the response time increase almost in proportion to the number of enrollees. A technique which decreases both of them using only scores has the advantage that it can be applied to any kind of biometric system that out- puts scores. In this paper, we propose such a technique by combining score level fusion and distance-based indexing. In order to reduce the retrieval error rate in multibiometric identification, our technique takes a strategy to select the template of the enrollee whose posterior probability of be- ing identical to the claimant is the highest as a next to be matched. The experimental evaluation using the Biosecure DS2 dataset and the CASIA-FingerprintV5 showed that our technique significantly reduced the identification error rates while keeping down or even reducing the number of score calculations, compared to the unimodal biometrics.