An Improved PCA Algorithm for Fast Face Recognition

The traditional Principal Component Analysis (PCA) can not satisfy the needs of practical application for the influence of the change of illumination, expression and so on. In this paper, an improved PCA algorithm is proposed for fast face recognition. First, after a candidate set being selected from all facial images, a Gabor filter is applied to the images in candidate set, meanwhile, the noise elimination algorithm is used. Then PCA algorithm is implemented for dimensionality reduction and feature extraction on these images. Finally the matched facial image is obtained by using the nearest neighbor classifier. Experiments in the ORL database show that the algorithm can not only shorten the recognition time, but also enhance the recognition rate greatly.