Two-dimensional weighted 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. Basically, in PCA the image always needs to be transformed into ID vector, however recently two-dimensional PCA (2DPCA) technique have been proposed. In 2DPCA, PCA technique is applied directly on the original images without transforming into ID vector. In this paper, we propose a new 2DPCA-based method that can improve the performance of the 2DPCA approach. 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 a new 2DPCA-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 2DPCA approach with the complexity nearly as same as that of 2DPCA method.