An Improved Face Recognition Technique Based on Modular Multi-directional Two-dimensional Principle Component Analysis Approach

In this paper, a new method named modular multi-directional two-dimensional principle component analysis (MD2DPCA) is proposed for face recognition. First, the original images are rotated at some predetermined angles so that we may extract features from the images in any direction. Then we divide the rotated images into smaller sub-images and apply 2DPCA approach to each of these sub-images. Finally we propose a fusion method named modular multi-directional 2DPCA (MD2DPCA) to combine a bank of preliminary results in different directions. Compared with conventional 2DPCA based algorithms, the advantage of the proposed method is that it can extract significant features from the images in any direction and avoid the effects of varying illumination and facial expression. The results of the experiments on ORL and Yale datasets show that the proposed MD2DPCA method can obtain a higher recognition rate than the conventional 2DPCA based methods.