Multimodal biometric system using face, ear and gait biometrics

In this paper, a novel multimodal biometric recognition system using three modalities including face, ear and gait, based on Gabor+PCA feature extraction method with fusion at matching score level is proposed. The performance of our approach has been studied under three different normalization methods (min-max, median-MAD and z-score) and two different fusion methods (weighted sum and weighted product). Our new method has been successfully tested using 360 images corresponding to 120 subjects from three databases including ORL face database, USTB ear database, and CASIA gait database. Because of these biometric traits, our proposed method requires no significant user cooperation and also can work from a long distance. According to the experimental results our proposed method exhibits excellent recognition performance and outperforms unimodal systems. The best recognition performance that our proposed method achieved is %97.5.