Face Recognition Using PCA and Minimum Distance Classifier

Face is the most easily identifiable characteristic of a person. Variations in facial expressions can be easily recognized by humans, while it is quite difficult for machines to recognize faces portraying varying facial expressions, pose, and illumination conditions efficiently. Face recognition works as a combination of feature extraction and classification. The selection of a combination of feature extraction technique and classifier to obtain maximum accuracy rate is a challenging task. This paper presents a unique combination of feature extraction technique and classifier that yields a satisfactory and more or less same accuracy rate when tested on more than one standard database. In this combination, features are extracted using principle coponent analysis (PCA). These extracted features are then fed to a minimum distance classification system. The proposed combination is tested on ORL and YALE datasets with an accuracy rate of 95.63% and 93.33%, respectively, considering variations in facial expressions, poses as well as illumination conditions.