Palmprint recognition using HMAX model and Support Vector Machine classifier
نویسندگان
چکیده
Support vector machine (SVM) and HMAX model are two powerful recent techniques. SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. In this paper we combine these two techniques for the palmprint verification problem. The Hong Kong Polytechnic University (PolyU) palmprint database that this database includes 600 palmprint images from 100 persons, with 6 images per person, is exploited to test our approach. Experimental results using the combination HMAX model and support vector machine (SVM) classifier obtains higher recognition rate than those obtained with HMAX model and knearest neighbors (KNN) classifier in identity verification system based on palmprint, and also demonstrated that the HMAX model, compared with PCA method, not only obtains higher recognition rate, but also this method is scale and rotate invariant, whereas PCA method provide high recognition rate only in closely controlled conditions.
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