Heterogeneous Face Matching Using ZigZag Pattern of Local Extremum Logarithm Difference: ZZPLELD

Abstract

A novel methodology for matching of heterogeneous faces, such as sketch-photo and near-infrared (NIR)-visible (VIS) images is proposed here. For heterogeneous face recognition, more emphasis is given to the edge features, which are invariant in different modality images. Since edges are sensitive to illuminations, we present an illumination-invariant image representation called local extremum logarithm difference (LELD). LELD provides illumination-invariant edge features in coarse level. Therefore, a local zigzag binary pattern LZZBP is presented to capture the local variation of LELD, and we call it a zigzag pattern of local extremum logarithm difference (ZZPLELD). We tested the proposed methodology on different sketch-photo and NIR-VIS benchmark databases. In the case of viewed sketches, the rank-1 recognition accuracy of 96.35% is achieved on CUFSF database. In the case of NIR-VIS matching, the rank-1 accuracy of 99.39% is achieved and which is superior to other state-of-the-art methods. We also tested ZZPLELD on illumination variation Extended Yale B database, and rank-1 recognition accuracy of 94.51% is achieved.