Robust Identification Based on Extracted Features With Visual Ventral Stream

Biometrics based personal identification is regarded as an effective method for automatic identification، with a high confidence coefficient. A multi modal biometric system consolidates the evidence presented by multiple biometric sources and typically provides better recognition performance compared to systems based on a single biometric modality. So in this paper we use combination of Face، Palm print and Ear characteristic to individual’s authentication. In our approach، features extracted using HMAX model are translation and scale-invariant. Then we applied Support vector machine and K-nearest neighbor classifiers to distinguish the classes. In fusion stage we use matching-score level. Experimental results showed 96% accuracy rate on ORL Face database and 96/6% accuracy rate on POLYU Palm print database and 97% accuracy rate on USTB Ear database; however we achieve 100% accuracy rate on Face، Palm print and Ear multi modal biometric.