Adapted deep convnets technology for robust iris recognition

Ying Chen; Wenyuan Wang; Zhuang Zeng; Yerong Wang

doi:10.1117/1.JEI.28.3.033008

15 May 2019 Adapted deep convnets technology for robust iris recognition

Ying Chen, Wenyuan Wang, Zhuang Zeng, Yerong Wang

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 3, 033008 (May 2019). https://doi.org/10.1117/1.JEI.28.3.033008

Abstract

Iris recognition (IR) is widely adopted for human identification in the biometric fields. However, results using handcrafted features designed with the domain knowledge are obtained by conventional IR algorithms which are complex. Convolutional neural networks (convnets) have been proved to be robust with superior learning capability in many computer vision fields. The deep convnets perform better than shallower convnets. However, training the deep convnets for IR is difficult. IrisConvDeeper, an adaptive architecture based on convnets combining Softmax classifier that is used to alleviate the problems of training deep convnets for IR, is proposed. In addition, a better performance is obtained. Furthermore, a shallower architecture termed as IrisConvShallower is designed for comparison. IrisConvDeeper consists of structures called dense block and has 12 or 14 convolutional layers. Meanwhile, IrisConvShallower consists of six or seven standard convolutional layers. The performance of the proposed architectures is tested on two public iris databases, which are collected under different conditions: CASIA-Iris-V3 and IITD iris image databases. The experimental results demonstrate that the proposed IrisConvDeeper outperforms most of state-of-the-art approaches in terms of correct recognition rate, except some approaches that adopted specific methods and identified fewer subjects compared with our methods.

Citation Download Citation

Ying Chen, Wenyuan Wang, Zhuang Zeng, and Yerong Wang "Adapted deep convnets technology for robust iris recognition," Journal of Electronic Imaging 28(3), 033008 (15 May 2019). https://doi.org/10.1117/1.JEI.28.3.033008

Received: 3 February 2019; Accepted: 16 April 2019; Published: 15 May 2019

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