Finger Vein De-noising Algorithm Based on Custom Sample-Texture Conditional Generative Adversarial Nets

• Published in: Neural processing letters Vol. 53; no. 6; pp. 4279 - 4292
• Main Authors: He, Bifeng, Shen, Lei, Wang, Huaxia, Yao, Yudong, Zhao, Guodong
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.12.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Complex Systems; Computational Intelligence; Computer Science; Datasets; Deep learning; Fingers; Neural networks; Noise; Partial differential equations; Random noise; Texture recognition; Custom sample-texture conditional generative adversarial nets; Loss function; Finger vein; Image de-noising; Texture features
• ISSN: 1370-4621; 1573-773X
• DOI: 10.1007/s11063-021-10589-5

Finger vein recognition is very important in the identity authentication, but its performance is affected significantly by noise. The widely used Conditional Generative Adversarial Nets (CGAN) de-noising algorithm without accurate texture constraints is easy to damage the texture features of the image. In this paper, we propose a finger vein de-noising algorithm based on Custom Sample-Texture Conditional Generative Adversarial Nets (CS-TCGAN). The proposed algorithm effectively protects the texture features while removing noise. Firstly, the proposed algorithm uses texture loss, adversarial loss, and content loss as constraints, which lead to a better de-noising performance on finger vein image with blurred texture.Secondly, in order to avoid the checkerboard artifacts effect caused by up-sampling in de-convolution process which results in the loss of the vein information, the dimension preserving structure is adopted in the generator network to minimize this problem. Lastly, the noise distribution of finger vein images obtained in the practical application has been investigated to generate the training dataset for obtaining a de-noising model with better generalization. Specifically, the training dataset has been established by combining Poisson noise, salt/pepper noise, Gaussian noise, and speckle noise. The experimental results illustrate that the performance of the proposed algorithm is better than the traditional filtering de-noising approaches and the widely used CGAN de-noising algorithms.