Markov Chains for unsupervised segmentation of degraded NIR iris images for person recognition

Unsupervised statistical models based on Hidden Markov Chain (HMC).Unconstrained segmentation of NIR iris images.Novel method for circular scanning of an image.Improvement of iris verification performance via HMC-based segmentation.New implementation relying down-sampled images for limiting the processing time. The iris segmentation module plays a crucial role in iris recognition system as it allows to define the exact iris texture region in the image of the eye. Usual iris segmentation methods tend to fail on challenging eye images captured in less constrained environment or at-a-distance. In this paper, we propose a new robust model to segment degraded iris images. Its main characteristics are as follows: (1) we explore the use of advanced statistical model for unsupervised iris segmentation and more particularly, we focused on Hidden Markov Chain. (2) Novel adequate image scanning procedure and initialization step for implementing this model are developed. (3) The implementation of the proposed model can be performed on reduced image resolutions allowing limiting the processing time without degradation of the performance. A novel recognition system can therefore be obtained by adding this unsupervised iris segmentation module as a preprocessing in the open-source recognition model OSIRIS-V4. Extensive experiments on two large near infra-red databases ICE2005 and CASIA-IrisV4-distance demonstrate a significant improvement of the recognition performance with this novel system compared to OSIRIS-V4 and recent region-based iris verification systems, showing this way the potential of such statistical models for iris recognition.