TY - JOUR
T1 - Finger vein image inpainting using neighbor binary-wasserstein generative adversarial networks (NB-WGAN)
AU - Jiang, Hanqiong
AU - Shen, Lei
AU - Wang, Huaxia
AU - Yao, Yudong
AU - Zhao, Guodong
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2022/7
Y1 - 2022/7
N2 - Traditional inpainting methods obtain poor performance for finger vein images with blurred texture. In this paper, a finger vein image inpainting method using Neighbor Binary-Wasserstein Generative Adversarial Networks (NB-WGAN) is proposed. Firstly, the proposed algorithm uses texture loss, reconstruction loss, and adversarial loss to constrain the network, which protects the texture in the inpainting process. Secondly, the proposed NB-WGAN is designed with a coarse-to-precise generator network and a discriminator network composed of two Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). The cascade of a coarse generator network and a precise generator network based on Poisson fusion can obtain richer information and get natural boundary connection. The discriminator consists of a global WGAN-GP and a local WGAN-GP, which enforces consistency between the entire image and the repaired area. Thirdly, a training dataset is designed by analyzing the locations and sizes of the damaged finger vein images in practical applications (i.e., physical oil dirt, physical finger molting, etc). Experimental results show that the performance of the proposed algorithm is better than traditional inpainting methods including Curvature Driven Diffusions algorithm without texture constraints, a traditional inpainting algorithm with Gabor texture constraints, and a WGAN inpainting algorithm based on attention mechanism without texture constraints.
AB - Traditional inpainting methods obtain poor performance for finger vein images with blurred texture. In this paper, a finger vein image inpainting method using Neighbor Binary-Wasserstein Generative Adversarial Networks (NB-WGAN) is proposed. Firstly, the proposed algorithm uses texture loss, reconstruction loss, and adversarial loss to constrain the network, which protects the texture in the inpainting process. Secondly, the proposed NB-WGAN is designed with a coarse-to-precise generator network and a discriminator network composed of two Wasserstein Generative Adversarial Networks with Gradient Penalty (WGAN-GP). The cascade of a coarse generator network and a precise generator network based on Poisson fusion can obtain richer information and get natural boundary connection. The discriminator consists of a global WGAN-GP and a local WGAN-GP, which enforces consistency between the entire image and the repaired area. Thirdly, a training dataset is designed by analyzing the locations and sizes of the damaged finger vein images in practical applications (i.e., physical oil dirt, physical finger molting, etc). Experimental results show that the performance of the proposed algorithm is better than traditional inpainting methods including Curvature Driven Diffusions algorithm without texture constraints, a traditional inpainting algorithm with Gabor texture constraints, and a WGAN inpainting algorithm based on attention mechanism without texture constraints.
KW - Finger vein
KW - Image inpainting
KW - Neighbor binary pattern (NBP) texture loss
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U2 - 10.1007/s10489-021-03017-7
DO - 10.1007/s10489-021-03017-7
M3 - Article
AN - SCOPUS:85122691978
SN - 0924-669X
VL - 52
SP - 9996
EP - 10007
JO - Applied Intelligence
JF - Applied Intelligence
IS - 9
ER -