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

• Published in: IEEE access Vol. 9; pp. 10120 - 10135
• Main Authors: Lee, Min Beom, Kang, Jin Kyu, Yoon, Hyo Sik, Park, Kang Ryoung
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Angle of reflection; Biometric recognition systems; Biometrics; Blurring; Cameras; deep learning; Feature extraction; generative adversarial network; Generative adversarial networks; Identification methods; Image enhancement; Image quality; Image recognition; Image reconstruction; Infrared cameras; Iris recognition; Near infrared radiation; Noise measurement; Object recognition; Specular reflection; Support vector machines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3050788

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.