Improved iris localization by using wide and narrow field of view cameras for iris recognition

Abstract

Biometrics is a method of identifying individuals by their physiological or behavioral characteristics. Among other biometric identifiers, iris recognition has been widely used for various applications that require a high level of security. When a conventional iris recognition camera is used, the size and position of the iris region in a captured image vary according to the X , Y positions of a user's eye and the Z distance between a user and the camera. Therefore, the searching area of the iris detection algorithm is increased, which can inevitably decrease both the detection speed and accuracy. To solve these problems, we propose a new method of iris localization that uses wide field of view (WFOV) and narrow field of view (NFOV) cameras. Our study is new as compared to previous studies in the following four ways. First, the device used in our research acquires three images, one each of the face and both irises, using one WFOV and two NFOV cameras simultaneously. The relation between the WFOV and NFOV cameras is determined by simple geometric transformation without complex calibration. Second, the Z distance (between a user's eye and the iris camera) is estimated based on the iris size in the WFOV image and anthropometric data of the size of the human iris. Third, the accuracy of the geometric transformation between the WFOV and NFOV cameras is enhanced by using multiple matrices of the transformation according to the Z distance. Fourth, the searching region for iris localization in the NFOV image is significantly reduced based on the detected iris region in the WFOV image and the matrix of geometric transformation corresponding to the estimated Z distance. Experimental results showed that the performance of the proposed iris localization method is better than that of conventional methods in terms of accuracy and processing time. © The Authors.Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.Subject terms: iris recognition; iris localization; wide field of view camera; narrow field of view camera; geometric transform.

Presentation Attack Detection for Iris Recognition System Using NIR Camera Sensor

“…We then accurately detected the iris boundaries based on the detection result of the pupil region using the CED method [ 25 , 26 , 27 ]. As shown in Figure 3 and Figure 4 , the center of the iris and pupil regions are pixels inside the bounding-box of the pupil region.…”