An efficient face recognition approach using PCA and minimum distance classifier

Facial expressions convey non-verbal cues, which play an important role in interpersonal relations. Automatic recognition of human face based on facial expression can be an important component of natural human-machine interface. It may also be used in behavioral science. Although human being can recognize the face practically without any effort, but reliable face recognition by machine is a challenge. This paper presents a new approach for recognizing the face of a person considering the expression of the same human face at different instances of time. This methodology is developed by combining principle component analysis (PCA) for feature extraction and minimum distance classifier (MDC) for classification. Experiment is done on AT&T dataset and the recognition rate achieves to 96.7% for different facial expressions.