Enhanced Hybrid Component-Based Face Recognition

Abstract

This paper presents a hybrid component-based face recognition. Can face recognition be enhanced by recognizing individual facial components: forehead, eyes, nose, cheeks, mouth and chin? The proposed technique implements texture descriptors Grey-Level Co-occurrence (GLCM) and Gabor Filters, shape descriptor Zernike Moments. These descriptors are effective facial components feature representations and are robust to illumination changes. Two classification techniques have been used and compared: Support Vector Machines (SVM) and Error-Correcting Output Code (ECOC). The experimental results obtained on three different facial databases, the FERET, FEI and CMU, show that component-based facial recognition is more effective than whole-face recognition.