Iris Localization using the Pupil Center Point based on Deep Learning in RGB Images (RGB 영상에서 딥러닝 기반 동공 중심점을 이용한 홍채 검출)

YongSoo Choi (이태균); Jang-Hee Yoo (유장희)

Iris Localization using the Pupil Center Point based on Deep Learning in RGB Images

Journal of Software Assessment and Valuation
Abbr : JSAV
2020, 16(2), pp.135-142
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : November 25, 2020
Accepted : December 21, 2020
Published : December 31, 2020

YongSoo Choi ¹, Jang-Hee Yoo ²

¹과학기술연합대학원대학교 ICT전공
²한국전자통신연구원

Candidate

ABSTRACT

In this paper, we describe the iris localization method in RGB images. Most of the iris localization methods are developed for infrared images, thus an iris localization method in RGB images is required for various applications. The proposed method consists of four stages: i) detection of the candidate irises using circular Hough transform (CHT) from an input image, ii) detection of a pupil center based on deep learning, iii) determine the iris using the pupil center, and iv) correction of the iris region. The candidate irises are detected in the order of the number of intersections of the center point candidates after generating the Hough space, and the iris in the candidates is determined based on the detected pupil center. Also, the error due to distortion of the iris shape is corrected by finding a new boundary point based on the detected iris center. In experiments, the proposed method has an improved accuracy about 27.4% compared to the CHT method.

KEYWORDS

iris localization, pupil detection, iris recognition, deep learning, hough transform

Citation status

* References for papers published after 2023 are currently being built.

[book] Kevin W. Bowyer / 2016 / Handbook of Iris Recognition / Springer

[journal] 고종국 / 2013 / 원거리 홍채인식 기술 동향 / 전자통신동향분석 28(3) : 67~75

[confproc] C. N. Padole / 2012 / Periocular Recognition : Analysis of Performance Degradation Factors / IAPR International Conference on Biometrics : 439~445

[journal] Hugo Proenca / 2010 / The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 32(8) : 1529~1535

[confproc] Z. Zhao / 2015 / An Accurate Iris Segmentation Framework under Relaxed Imaging Constraints using Total Variation Model / Proceeding of the IEEE International Conference on Computer Vision : 3828~3836

[journal] J. Daugman / 2001 / Statistical Richness of Visual Phase Information : Update on Recognizing Persons by Iris Patterns / Int'l Journal of Computer Vision 45(1) : 25~38

[journal] L. Ma / 2004 / Efficient Iris Recognition by Characterizing Key Local Variations / IEEE Transactions on Image Processing / Institute of Electrical and Electronics Engineers (IEEE) 13(6) : 739~750

[journal] M. Arsalan / 2017 / Deep Learning-based Iris Segmentation for Iris Recognition in Visible Light Environment / Symmetry 9(11) : 263~

[journal] F.J. Vera-Olmos / 2018 / DeepEye: Deep convolutional network for pupil detection in real environments / Integrated Computer-Aided Engineering / IOS Press 26(1) : 85~95

[journal] G. J. Mohammed / 2012 / Accurate Pupil Features Extraction based on New Projection Function / Computing and Informatics 29(4) : 663~680

[journal] Liang-Chieh Chen / 2018 / DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs / IEEE Transactions on Pattern Analysis and Machine Intelligence / Institute of Electrical and Electronics Engineers (IEEE) 40(4) : 834~848

[journal] Noureddine Cherabit / 2012 / Circular Hough Transform for Iris localization / Science and Technology / Scientific and Academic Publishing 2(5) : 114~121

KJCKorea
Journal Central

Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35