Improved weight assignment approach for multimodal fusion

Biometric systems are emerging trends in the world of technological revolution for accessing confidential information of an individual. Multibiometric systems are more preferable nowadays as it overcomes the problem of non-universality, increases recognition performance and spoofing faced by the unimodal system. The multimodal biometric system is built up by fusing the features/ scores/ decisions of individual model. Fusion at the match score level is more preferable as it is more efficient in terms of computational complexity and contains sufficient information to recognize an individual. In the proposed bimodal rule based method, weight assignment strategy is based upon the Equal Error Rate (EER) and Genuine Acceptance Rate (GAR) values of individual models. It has been observed that total error rate (TER) of a fused system decreases and the Genuine Acceptance Rate (GAR) of fused system increases i.e. recognition performance of fused system is better as compared to individual systems.