Modular PCA based on Within-Class median for face recognition

Aiming at the problem that recognition rate of Principal Component Analysis (PCA) algorithm is low in face recognition, this paper proposes a modular PCA algorithm based on Within-Class median. Firstly, within-class median of each sub-image of all training samples in each class are calculated, and they are used to normalize each corresponding sub-image of within-class sample. After that, the best projecting matrix from general matrix that is made up of all normalized sub-images can be obtained accordingly. Secondly, when all sub-images of training samples and testing samples are projected to the best projecting matrix that has been got above, the recognition features is produced; Finally, the nearest distance classifier is used to distinguish each face. Experiment results on ORL face database indicate that the recognition performance of the algorithm is superior to that of general modular PCA algorithm.