Multimodal biometric recognition inspired by visual cortex and Support vector machine classifier

Abstract

Biometrics based personal identification is regarded as an effective method for automatic identification, with a high confidence coefficient. A multimodal 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 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 (SVM) and K-nearest neighbor (KNN) classifiers to distinguish the classes. In fusion stage we use matching-score level. Experimental results showed 96% accuracy rate on ORL Face database and 94% accuracy rate on USTB Ear database; however we achieve 98% accuracy rate on Face and Ear multimodal biometric.