Cross-Spectral Iris Matching for Surveillance Applications

With the advancement in iris recognition at a distance, cross-spectral iris matching is emerging as a hot topic. The importance of cross-spectral matching stems from the feasibility of performing matching in several security applications such as watch-list identification, security surveillance and hazard assessment. Typically, a person’s iris images are captured under Near-Infrared light (NIR) but most of the security cameras operate in the Visible Light (VL) spectrum. In this work, we therefore propose two methods for cross-spectral iris recognition capable of matching iris images in different lighting conditions. The first method is designed to work with registered iris images. The key idea is to synthesize the corresponding NIR images from the VL images using Artificial Neural Networks (ANN). The second one is capable of working with unregistered iris images based on integrating the Gabor filter with different photometric normalization models and descriptors along with decision level fusion to achieve the cross-spectral matching. Experimental and comparative results on the UTIRIS and the PolyU databases demonstrate that the proposed methods achieve promising results. In addition, the results indicate that the VL and NIR images provide complementary features for the iris pattern and their fusion improves the recognition performance.