Improved Cross-Spectral Iris Matching Using Multi-Scale Weberface and Gabor Local Binary Pattern

Iris matching is widely used in various biometric systems, such as for person identification and verification. Cross-spectral iris matching is defined as the matching between two iris images acquired in different electromagnetic spectra. One of the problems in cross-spectral iris matching is the modality gap between the images. Therefore, feature descriptors that are invariant to illumination and generate higher accuracy are required. This paper proposes an improved cross-spectral iris matching framework using Multi-Scale Weberface (MSW). In the proposed method, MSW is integrated with the Gabor Local Binary Pattern (GLBP), and it is abbreviated as MGLBP. Experimental results demonstrate that the proposed framework outperforms the previous method with higher accuracy. By using MGLBP, the recognition rate increases by 40% and 9% compared with the LBP and GLBP, respectively.