Improvement on PCA and 2DPCA Algorithms for Face Recognition

Abstract

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 new PCA-based methods that can improve the performance of the traditional PCA and two-dimensional PCA (2DPCA) approaches. In face recognition where the training data are labeled, a projection is often required to emphasize the discrimination between the clusters. Both PCA and 2DPCA 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 new PCA-based schemes 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 and 2DPCA approaches with the complexity nearly as same as that of PCA and 2DPCA methods.