FACE RECOGNITION USING HYBRID SIFTSVM

Abstract

Face recognition provides a challenging issue in the domain of analyzing images. In this dissertation, a face recognition model using hybrid SIFT-SVM is presented and a comparative analysis between SVM and hybrid SIFT-SVM has been studied. The current database is divided into two various parts, training and testing database. The SIFT feature will be created for each training images and the key points are computed, then the SVM is applied for the matching process for test images. Results are obtained for three cases child, adult and old age which are made on the basis of age. The recognition rate has been computed by False Acceptance Rate (FAR) and False Rejection Rate (FRR) on these cases and then the results of hybrid SIFT-SVM is compared with SVM. It has been studied that the recognized result provides robust performance under various conditions like different pose, lighting conditions and facial expressions.