Iris double recognition based on modified evolutionary neural network

Shuai Liu; Yuan-Ning Liu; Xiao-Dong Zhu; Guang Huo; Wen-Tao Liu; Jia-Kai Feng

doi:10.1117/1.JEI.26.6.063023

9 December 2017 Iris double recognition based on modified evolutionary neural network

Shuai Liu, Yuan-Ning Liu, Xiao-Dong Zhu, Guang Huo, Wen-Tao Liu, Jia-Kai Feng

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 6, 063023 (December 2017). https://doi.org/10.1117/1.JEI.26.6.063023

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.

Citation Download Citation

Shuai Liu, Yuan-Ning Liu, Xiao-Dong Zhu, Guang Huo, Wen-Tao Liu, and Jia-Kai Feng "Iris double recognition based on modified evolutionary neural network," Journal of Electronic Imaging 26(6), 063023 (9 December 2017). https://doi.org/10.1117/1.JEI.26.6.063023

Received: 23 August 2017; Accepted: 14 November 2017; Published: 9 December 2017

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