Iris segmentation is a critical step in the entire iris recognition procedure. Most of the state-of-the-art iris segmentation algorithms are based on edge information. However, a large number of noisy edge points detected by a normal edge-based detector in an image with specular reflection or other obstacles will mislead the pupillary boundary and limbus boundary localization. In this paper, we present a combination method of learning-based and edge-based algorithms for iris segmentation. A well-designed Faster R-CNN with only six layers is built to locate and classify the eye. With the bounding box found by Faster R-CNN, the pupillary region is located using a Gaussian mixture model. Then, the circular boundary of the pupillary region is fit according to five key boundary points. A boundary point selection algorithm is used to find the boundary points of the limbus, and the circular boundary of the limbus is constructed using these boundary points. Experimental results showed that the proposed iris segmentation method achieved 95.49% accuracy on the challenging CASIA-Iris-Thousand database.

In modern society, mobile devices (such as smart phones and wearable devices) have become indispensable to almost everyone, and people store personal data in devices. Therefore, how to implement user authentication mechanism for private data protection on mobile devices is a very important issue. In this paper, an intelligent iris recognition mechanism is designed to solve the problem of user authentication in wearable smart glasses. Our contributions include hardware and software. On the hardware side, we design a set of internal infrared camera modules, including well-designed infrared light source and lens module, which is able to take clear iris images within 2~5 cm. On the software side, we propose an innovative iris segmentation algorithm which is both efficient and accurate to be used on smart glasses device. Another improvement to the traditional iris recognition is that we propose an intelligent Hamming distance (HD) threshold adaptation method which dynamically fine-tunes the HD threshold used for verification according to empirical data collected. Our final system can perform iris recognition with 66 frames per second on a smart glasses platform with 100% accuracy. As far as we know, this system is the world’s first application of iris recognition on smart glasses.