Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction

被引：19
作者：
Lee, Min Beom [1 ]
Kang, Jin Kyu [1 ]
Yoon, Hyo Sik [1 ]
Park, Kang Ryoung [1 ]
机构：
[1] Dongguk Univ, Div Elect & Elect Engn, Seoul 04620, South Korea
来源：
IEEE ACCESS | 2021年 / 9卷
基金：
新加坡国家研究基金会;
关键词：
Iris recognition; Feature extraction; Cameras; Databases; Image recognition; Support vector machines; Noise measurement; Biometrics; iris recognition; deep learning; generative adversarial network; COLOR;
D O I：
10.1109/ACCESS.2021.3050788
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Iris recognition is one of the non-contact biometric identification methods that are hygienic and highly accurate. Iris recognition involves using iris images obtained by a near-infrared (NIR) camera or a visible light camera. A clear image of iris can be obtained when an NIR camera is used, but it requires an NIR illuminator in addition to the NIR camera. Iris recognition can be performed with a built-in camera device when a visible light camera is used, which also has the advantage of obtaining a three-channel image containing the color information. Accordingly, studies are being conducted on iris recognition by obtaining iris images from the face images taken by a high-resolution visible light camera in smartphones. However, when iris images have unconstrained conditions or are obtained without the cooperation of the subjects, the quality of iris images are reduced by noises such as optical and motion blur, off-angle view, specular reflection (SR), and other artifacts, thus ultimately deteriorating the recognition performance. Therefore, in this study, a method has been proposed for enhancing the quality of iris images by blurring the iris region and deep-learning-based deblurring. In addition, we propose the method for improving the recognition performance by integrating the recognition score in periocular regions and support vector machine (SVM). The method proposed in this study, which was experimented with noisy iris challenge evaluation-part II training database and MICHE database, exhibited an improved performance compared to the state-of-the-art methods.
引用
收藏
页码：10120 / 10135
页数：16
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