Face Recognition Method by Using Large and Representative Datasets

A face recognition method by using large and representative datasets is presented in this paper. The importance of research on face recognition is fueled by both its scientific challenges and its potential applications. In this contribution, we proposes several approaches to deal with some of the difficulties that one encounters when trying to recognize frontal faces in unconstrained domains and when only one sample per class is available to the learning system. It is possible for an automatic recognition system to compensate for imprecisely localized, partially expression variant faces even when only one single training sample per class is available. Finally, we have shown that the results of an appearance-based approach totally depend on the differences that exist between the facial expressions displayed on the learning and testing images.