TY - JOUR
T1 - Enhanced Iris Recognition Method by Generative Adversarial Network-Based Image Reconstruction
AU - Lee, Min Beom
AU - Kang, Jin Kyu
AU - Yoon, Hyo Sik
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Biometrics
KW - deep learning
KW - generative adversarial network
KW - iris recognition
UR - http://www.scopus.com/inward/record.url?scp=85099557656&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3050788
DO - 10.1109/ACCESS.2021.3050788
M3 - Article
AN - SCOPUS:85099557656
SN - 2169-3536
VL - 9
SP - 10120
EP - 10135
JO - IEEE Access
JF - IEEE Access
M1 - 9319650
ER -