Optimal fusion of multimodal biometric authentication using wavelet probabilistic neural network

In order to enhance security and protection capability, the integration of different biometric features to set up multimodal biometric authentication system is an effective way. It can provide complementary information to enhance recognition rate, and it can further enhance the reliability and stability of the identity authentication system. However, although the use of multimodal biometric feature has the advantage to maintain the maximal entropy, yet it will also affect at the same time the training result and operation performance of the classifier at the back end. In this study, we have associated face feature and iris feature to set up multimodal biometric feature vector with high identification rate, meanwhile, PSO is used to perform the optimization design of WPNN classifier architecture so as to realize high performance classifier applicable to multimodal biometric authentication. From the experimental results, it can be proved that the multimodal biometric authentication system as mentioned in this paper, in addition to possessing the feature of reliability and correctness, has also excellent characteristics such as simplified feature vector and fast operation, in other words, it has pretty high practical value.