Person identification using face and speech
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In this thesis, we present a multimodal biometric system using face and speech features. Multimodal biometrics system uses two or more intrinsic physical or behaviour traits to provide better recognition rate than unimodal biometric systems. Face recognition is built using principal component analysis (PCA) and the Gabor filters. In Face recognition, PCA is applied to Gabor filter bank response of the face images. Speaker recognition is built using amplitude modulation - frequency modulation (AM-FM) features. AM-FM features are weighted-instantaneous frequency of the analytical signal. Finally, weighted sum of score of face and speaker recognition system is used for person identification. Performance of our system is evaluated by using ORL database for face images and ELSDSR database for speech. Experimental results show better recognition rate for the multimodal sytem when compared to unimodal system
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