Regular Articles

Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network

[+] Author Affiliations
Fei He

Northeast Normal University, School of Computer Science and Information Technology, Changchun, China

Northeast Normal University, School of Environment, Changchun, China

Northeast Normal University, Institute of Computational Biology, Changchun, China

Ye Han, Yuanning Liu

Jilin University, College of Computer Science and Technology, Changchun, China

Jilin University, Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Changchun, China

Han Wang, Jinchao Ji, Zhiqiang Ma

Northeast Normal University, School of Computer Science and Information Technology, Changchun, China

Northeast Normal University, Institute of Computational Biology, Changchun, China

J. Electron. Imaging. 26(2), 023005 (Mar 10, 2017). doi:10.1117/1.JEI.26.2.023005
History: Received October 30, 2016; Accepted February 23, 2017
Text Size: A A A

Abstract.  Gabor filters are widely utilized to detect iris texture information in several state-of-the-art iris recognition systems. However, the proper Gabor kernels and the generative pattern of iris Gabor features need to be predetermined in application. The traditional empirical Gabor filters and shallow iris encoding ways are incapable of dealing with such complex variations in iris imaging including illumination, aging, deformation, and device variations. Thereby, an adaptive Gabor filter selection strategy and deep learning architecture are presented. We first employ particle swarm optimization approach and its binary version to define a set of data-driven Gabor kernels for fitting the most informative filtering bands, and then capture complex pattern from the optimal Gabor filtered coefficients by a trained deep belief network. A succession of comparative experiments validate that our optimal Gabor filters may produce more distinctive Gabor coefficients and our iris deep representations be more robust and stable than traditional iris Gabor codes. Furthermore, the depth and scales of the deep learning architecture are also discussed.

Figures in this Article
© 2017 SPIE and IS&T

Citation

Fei He ; Ye Han ; Han Wang ; Jinchao Ji ; Yuanning Liu, et al.
"Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network", J. Electron. Imaging. 26(2), 023005 (Mar 10, 2017). ; http://dx.doi.org/10.1117/1.JEI.26.2.023005


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement


 

  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.