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Iris double recognition based on modified evolutionary neural network
Author(s): Shuai Liu; Yuan-Ning Liu; Xiao-Dong Zhu; Guang Huo; Wen-Tao Liu; Jia-Kai Feng
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Paper Abstract

Aiming at multicategory iris recognition under illumination and noise interference, this paper proposes a method of iris double recognition based on a modified evolutionary neural network. An equalization histogram and Laplace of Gaussian operator are used to process the iris to suppress illumination and noise interference and Haar wavelet to convert the iris feature to binary feature encoding. Calculate the Hamming distance for the test iris and template iris , and compare with classification threshold, determine the type of iris. If the iris cannot be identified as a different type, there needs to be a secondary recognition. The connection weights in back-propagation (BP) neural network use modified evolutionary neural network to adaptively train. The modified neural network is composed of particle swarm optimization with mutation operator and BP neural network. According to different iris libraries in different circumstances of experimental results, under illumination and noise interference, the correct recognition rate of this algorithm is higher, the ROC curve is closer to the coordinate axis, the training and recognition time is shorter, and the stability and the robustness are better.

Paper Details

Date Published: 9 December 2017
PDF: 11 pages
J. Electron. Imag. 26(6) 063023 doi: 10.1117/1.JEI.26.6.063023
Published in: Journal of Electronic Imaging Volume 26, Issue 6
Show Author Affiliations
Shuai Liu, Jilin Univ. (China)
Yuan-Ning Liu, Jilin Univ. (China)
Xiao-Dong Zhu, Jilin Univ. (China)
Guang Huo, Northeast Electric Power Univ. (China)
Wen-Tao Liu, Jilin Univ. (China)
Jia-Kai Feng, Jilin Univ. (China)

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