From coarse to fine: Two-stage deep residual attention generative adversarial network for repair of iris textures obscured by eyelids and eyelashes

Ying Chen; Yugang Zeng; Liang Xu; Shubin Guo; Ali Asghar Heidari; Huiling Chen; Yudong Zhang

doi:10.1016/j.isci.2023.107169

From coarse to fine: Two-stage deep residual attention generative adversarial network for repair of iris textures obscured by eyelids and eyelashes

iScience. 2023 Jun 21;26(7):107169. doi: 10.1016/j.isci.2023.107169. eCollection 2023 Jul 21.

Authors

Ying Chen¹, Yugang Zeng¹, Liang Xu¹, Shubin Guo¹, Ali Asghar Heidari², Huiling Chen³, Yudong Zhang⁴

Affiliations

¹ School of Software, Nanchang Hangkong University, Nanchang, Jiangxi 330063, China.
² School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
³ Key Laboratory of Intelligent Informatics for Safety & Emergency of Zhejiang Province, Wenzhou University, Wenzhou 325035, PR China.
⁴ School of Computing and Mathematical Sciences, University of Leicester, LE1 7RH Leicester, UK.

Abstract

We propose a two-stage deep residual attention generative adversarial network (TSDRA-GAN) for inpainting iris textures obscured by eyelids. This two-stage generation approach ensures that the semantic and texture information of the generated images is preserved. In the second stage of the fine network, a modified residual block (MRB) is used to further extract features and mitigate the performance degradation caused by the deepening of the network, thus following the concept of using a residual structure as a component of the encoder. In addition, for the skip connection part of this phase, we propose a dual-attention computing connection (DACC) to computationally fuse the features of the encoder and decoder in both directions to achieve more effective information fusion for iris inpainting tasks. Under completely fair and equal experimental conditions, it is shown that the method presented in this paper can effectively restore original iris images and improve recognition accuracy.

Keywords: Health sciences; Medicine; Optometry.