An effective non-cooperative iris recognition system using hierarchical collaborative representation-based classification

In recent years, non-cooperative iris recognition has gained a major role in biometric authentication system. However, owing to images captured in non-cooperative environments, it is quite a difficult job, for these images have specular reflections, blur, occlusions, and are off-axis. This research introduces an efficient non-cooperative iris recognition system developed for hierarchical collaborative representation-based classifier (HCRC). The proposed method includes three stages. The first stage involves image preprocessing. Initially, hybrid median filtering is done to reduce noise and to improve the image quality. Then, segmentation of the abnormal non-cooperative iris image is carried out by applying Geodesic Region-based Active Contour Level-set algorithm and threshold-based segmentation algorithm. In the second stage, 2 x 2 block-based Local Ternary Pattern (LTP) is applied to the segmented image. This gives upper and lower LTP histogram blocks. The final feature vector is obtained by combining these two blocks. In the third stage, the feature vectors are applied to the HCRC for classification on the basis of the iris database. The proposed iris recognition technique proved itself by achieving 98.60, 0.095 and 0.096 accuracy, false acceptance rate and false rejection rate, respectively.