Ear recognition using features inspired by visual cortex and support vector machine technique

Ear is a new class of relatively stable biometric that is invariant from childhood to early old age. In most cases techniques already working in other biometric fields, such as PCA are applied to ear. Eigen-ears provide high recognition rate only in closely controlled conditions. Indeed, even a slight amount of rotation can cause a significant drop in system performance and in unattended systems rotations occur very frequently. HMAX is a feature extraction method and this method is motivated by a quantitative model of visual cortex. Also, SVMs are classifiers which have demonstrated high generalization capabilities in many different tasks, including the object recognition problem. In this paper we combine these two techniques for the robust Ear verification problem. The USTB database is exploited to test our approach. Experimental results using the combination HMAX model and support vector machine (SVM) classifier (with kernel=1), obtains higher recognition rate than those obtained with HMAX model and k-nearest neighbors classifier in ear verification. In addition to, demonstrated that this method is rotate-and scale-invariant, and also, in experiment, it was found that, using of Gaussian filter in HMAX model in compared to using of Gabor filter, increases performance of ear recognition.