Optimal feature level fusion for secured human authentication in multimodal biometric system

The rising demand for high security and reliable authentication schemes, led to the development of the unimodal biometric system so that the multimodal biometric system has emerged. The multimodal biometric system will use more than one biometric trait of an individual for identification and security purpose. Fusion plays a major role in the multimodal biometric system. Several fusion techniques are used in biometric systems. Feature level fusion is a very much popular method as compared to the other fusion techniques. In this fusion, features are extracted from all biometric traits. After that extracted features are combined into a final feature vector of high dimension. In this paper, we introduce a new technique to perform fusion at the feature level by optimal feature level fusion; here the relevant features are selected using an optimization technique. Here, we proposed OGWO for selecting optimal features. Moreover, we suggested the recognition technique. For recognition, we use the multi-kernel support vector machine algorithm. Finally, the performance of our proposed method is evaluated by some evaluation measures. Our recommended method is implemented in the MATLAB platform.