Recognition of occluded faces using an enhanced EBGM algorithm

A new approach to recognize occluded faces is presented in this paper to enhance the conventional Elastic Bunch Graph Matching (EBGM) technique. In the conventional EBGM approach, facial landmarks need to be chosen manually in the initial stage and a single graph per face had been modeled. Our proposed approach intuitively fuses a Harmony search based optimization algorithm over the EBGM approach to automatically choose optimal land marks for a given face. Further, instead of using a single graph, we deploy component level sub-graphs and locate optimal landmarks by maximizing the similarity between each of the sub-graphs. Experimental results show that the proposed automatic method achieves an improved recognition rate when compared to the conventional EBGM approach.