Hierarchical Fusion of Face and Iris for Personal Identification

Most existing face and iris fusion schemes are concerned about improving performance on good quality images under controlled environments. In this paper, we propose a hierarchical fusion scheme for low quality images under uncontrolled situations. In the training stage, canonical correlation analysis (CCA) is adopted to construct a statistical mapping from face to iris in pixel level. In the testing stage, firstly the probe face image is used to obtain a subset of candidate gallery samples via regression between the probe face and gallery irises, then ordinal representation and sparse representation are performed on these candidate samples for iris recognition and face recognition respectively. Finally, score level fusion via min-max normalization is performed to make final decision. Experimental results on our low quality database show the outperforming performance of proposed method.