Face Recognition Based on Multiple Region Features

For face recognition, face feature selection is an important step. Better features should result in better performance. This paper describes a robust face recognition algorithm using multiple face region features selected by the AdaBoost algorithm. In conventional face recognition algorithms, the face region is dealt with as a whole. In this paper we show that dividing a face into a number of sub-regions can improve face recognition performance. We use conventional AdaBoost with a weak learner based on multiple region orthogonal component principal component analysis (OCPCA) features. The regions are selected areas of the face (such as eye, mouth, nose etc.). The AdaBoost algorithm generates a strong classifier from the combination of these region features. Experiments have been done to evaluate the performance on the CMU Pose Illumination Expression (PIE) databases. Performance comparisons between single region OCPCA, our multiple region OCPCA, and published results from Visionics’ FaceIt are given. Significant performance improvement is demonstrated using multiple facial region OCPCA features.