Application of image correction and bit-plane fusion in generalized PCA based face recognition

A novel generalized PCA based face recognition algorithm is proposed in this paper. Two approaches to improve the illumination robustness of the algorithm are presented, symmetrical image correction (SIC) and bit-plane feature fusion (BPFF). Specifically, for an assumed eudipleural face image, SIC first compares a pixel with the mean of this pixel and its symmetrical one and constructs a weight using the difference, then performs correction of the face image by adding the weight image to it to reduce bright speckles and shadows caused by over lighting. BPFF decomposes a face image into its eight bit-planes and extracts outline features and texture features respectively from them, then it constructs a new virtual face by combining those two features. Finally, Generalized PCA is applied to the virtual faces to achieve face recognition. Experimental results show that, the proposed combined approach can effectively reduce the sensitivity of face recognition algorithm to illumination variances and thus fewer projection vectors are required to achieve the same recognition rate than the comparing approaches.