Abstract
Face recognition is one of the crucial and important methods for the security purposes. The accuracy of the facial recognition system degrades over time. Therefore the FRS system must be up-to-date. Sometimes the system will not be up-to-date because of unlabelled face images, or because of change in the facial expressions or because of any relevant changes in face of person. Therefore in such cases also the FRS must be self-trained to make the system up-to-date. In this paper a semi-supervised version, based on the selftraining method, of the classical PCA-based face recognition algorithm is proposed to exploit unlabelled data for off-line updating of the Eigen space and the templates. Reported results show that the exploitation of unlabelled data by self-training can substantially improve the performances achieved with a small set of labelled training examples. Index Terms: Eigen faces, Face recognition, face classifier, occluded faces
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