Improve recognition performance by hybridizing principal component analysis (PCA) and elastic bunch graph matching (EBGM)

In this paper, a new type of hybrid method that hybridizes PCA and EBGM as a two-stage procedure is presented to improve recognition performance in large-scale face recognition. Among various methods in face recognition, PCA is considered to identify human faces by holistic views, while EBGM is supposed to distinguish one face from another by details, but they are both excellent representative methods due to their respective advantages. However, when the size of gallery gets large, the recognition performance of both PCA and EBGM degrades severely. To improve recognition performance with large-scale gallery, we propose a hybrid method, which preprocesses the gallery images with PCA at first stage, and produces the final result with EBGM based on the preliminary result generated by PCA. Since the hybrid method combines the advantages of PCA and EBGM, the recognition performance with large-scale gallery has been improved greatly. Experimental result shows that the hybrid method has a remarkably better recognition accuracy than either PCA or EBGM. Moreover, it seems that the larger the gallery size, the better the improvement. On the other hand, the hybrid method brings no additional computational cost, even less than EBGM.