Kernel-based scale-invariant feature transform and spherical SVM classifier for face recognition
Face recognition system is a computer application which is capable of measuring and matching the distinctive features intended for the purpose of identifying or verifying a person from a digital image. In digital or connected cameras, face recognizing system detects the faces in the image and measure their characteristics. It is then matched with the templates that are stored in a database. Nowadays, automated face recognition system is a relatively new concept. In this paper, we propose kernel based Scale Invariant Feature Transform and spherical SVM classifier for face recognition. Furthermore, a novel weightage function for feature extraction and classification, which is termed as Multi Kernel Function (MKF) is also proposed. To extract facial features, we adopt SIFT technique, which is modified in the descriptor stage by the proposed MKF weightage function, thereby evolving a new technique which we termed as KSIFT. Multi-kernel Spherical SVM classifier is used for the classification purpose. The experimental results of our proposed system are evaluated and we analysed the recognition performance by the metrics such as FAR, FRR and Accuracy. Then, the performance is also compared with the existing systems like HOG, SIFT and WHOG and our proposed method attains the higher accuracy of 99% for the face recognition system.