Heterogeneous face matching using geometric edge-texture feature (GETF) and multiple fuzzy-classifier system

A novel and accurate method for matching of heterogeneous faces, such as sketch and near-infrared (NIR) images, with the visible (VIS) photo gallery and vice a versa has been presented here. A new geometric edge-texture feature (GETF) is proposed, which is not only able to capture the edge information but also the texture information. GETF is constructed from the combined information of edge and texture image of same individual. For texture information local binary pattern (LBP) is used, while for edge information canny edge detection is chosen. Edges are sensitive to illumination, so before applying canny edge operation, we convert the image into illumination invariant gradient domain. For each pixel of the edge image, the nearest edge pixel is found. Finally, the total hamming distance between any pixel and its nearest edge pixel of the corresponding texture image gives GETF_Dist and the angle between them gives the GETF_Ang feature. To classify the heterogeneous faces we proposed a multiple fuzzy-classifier system, which is a combination of fuzzy partial least square (FPLS) and fuzzy local feature-based discriminant analysis (FLFDA). We have tested statistically that, the proposed classifier performs better than the individual classifiers. In sketch-photo matching, a rank-1 accuracy of 99.66% is achieved in a gallery of 606 photos consisting of CUHK student dataset, AR face dataset, and XM2VTS dataset. In NIR–VIS image matching, a rank-1 accuracy of 99.50% is achieved in a gallery of 400 VIS images from CASIA-HFB dataset.