Multibiometric Authentication Using DWT and Score Level Fusion
Multimodal system, which combines more than one biometric trait, gives better recognition performance compare to system based on a single biometric modality. It obtains today's increasing demand of high accuracy in biometric system. This paper proposes a multimodal biometric system using two traits i.e. face and palm print for person recognition. In this proposed method use an efficient technique for an authentication with an average half face and selected window size palm print features. Integrating the features increases robustness of the person authentication. Discrete Wavelet Transform (DWT) is used for feature extraction and Support Vector Machine (SVM) is proposed for classification. The final decision is made by fusion at matching score level.