An Improvement on PCA Algorithm for Face Recognition

Principle Component Analysis (PCA) technique is an important and well-developed area of image recognition and to date many linear discrimination methods have been put forward. Despite these efforts, there persist in the traditional PCA some weaknesses. In this paper, we propose a new PCA-based method that can overcome one drawback existed in the traditional PCA method. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. PCA may fail to accomplish this, no matter how easy the task is, as they are unsupervised techniques. The directions that maximize the scatter of the data might not be as adequate to discriminate between clusters. So we proposed a new PCA-based scheme which can straightforwardly take into consideration data labeling, and makes the performance of recognition system better. Experiment results show our method achieves better performance in comparison with the traditional PCA method.