Recognition of facial images using support vector machines

Abstract

A novel support vector machine (SVM)-based method for appearance-based face recognition is presented. The proposed method does not use any external feature extraction process. Accordingly the intensities of the raw pixels that make up the face pattern are fed directly to the SVM. However, it takes account of prior knowledge about facial structures in the form of a kernel embedded in the SVM architecture. The new kernel efficiently explores spatial relationships among potential eye, nose, and mouth objects and is compared with existing kernels. Experiments with ORL database show a recognition rate of 98% and speed of 0.22 seconds per face with 40 classes.