An efficient multimodal person authentication system using Gabor and sub band coding

This paper presents fusion of two biometric traits, i.e., face and speech, at matching score level fusion. The features are extracted from the pre-processed images of face and speech. Gabor Wavelet and Discrete Cosine Transform (DCT) are used to extract facial features and Sub Band Coding (SBC) is used to extract features from speech signals. These features of a probe image are compared with training images of each trait and then calculate matching score. The individual scores generated after each matching are passed to the fusion module. The final score is then used to declare the person as genuine or an impostor. The proposed method is tested on ORL database and it outperforms with False Acceptance Rate of 0.75% and False Rejection Rate of 1.24%.