9 December 2017 Iris double recognition based on modified evolutionary neural network
Author Affiliations +
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.
© 2017 SPIE and IS&T
Shuai Liu, Yuan-Ning Liu, Xiao-Dong Zhu, Guang Huo, Wen-Tao Liu, 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 . Submission: Received: 23 August 2017; Accepted: 14 November 2017
Received: 23 August 2017; Accepted: 14 November 2017; Published: 9 December 2017
JOURNAL ARTICLE
11 PAGES


SHARE
Back to Top