An Accurate and Efficient User Authentication Mechanism on Smart Glasses Based on Iris Recognition

Abstract

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.

An Efficient and Robust Iris Segmentation Algorithm Using Deep Learning

“…e limbus boundary was estimated after the pupillary region, and its boundary was located. e enhanced version of MIGREP [9] was applied for estimating the coarse limbus boundary. Its required work was to design a few radially emitting paths that went outward from the pupillary center.…”

“…e other was defined as s 2 and represented the distance from the pupillary center to the end points of the emitting paths. In [9], these two parameters were predefined and cannot adapt to various input images during runtime. In this work, (s 1 , s 2 ) were dynamically adjusted according to the size of the bounding box found by Faster R-CNN.…”

“…Once the potential bounding boxes of the eye are obtained, a pretrained Gaussian mixture model (GMM) [7,8] is used to fit the pupillary region. Secondly, an improved limbus boundary localization algorithm [9] is applied to find the limbus boundary points. irdly, the iris region is located by identifying the pupillary and limbus boundaries.…”

Information Propagation Prediction Based on Key Users Authentication in Microblogging

“…As shown in Figure 6, the retweet scale prediction equation is defined as (5). Equation (5) includes three parts.…”

“…A mass of research [1][2][3][4][5] focuses on passwords mechanism and other authentication mechanisms for user authentication in various computer systems. But recently, with the rapid development of microblogging, the scale of users is becoming larger and larger.…”