Abstract
Palmprint has a high application prospect due to the stability, uniqueness, difficulty of reproduction, easy acquisition and high user acceptance of its own texture characteristics. However, palmprint images acquired in low-illumination conditions can lose a large amount of palmprint texture features, resulting in distortion of the palmprint image. In this paper, an improved U-Net neural network palmprint image enhancement algorithm is designed(SCAU-Net), that is, the depth and structure adjustment is made on the traditional U-Net neural network and the output feature of a hybrid attention mechanism adjustment is added to the jump connection to solve the problem that the palmprint image quality is easily affected by light intensity. The proposed method in this paper is experimented on the palmprint databases such as Idiap, CASIA, IITD and the laboratory self-acquisition, and the PSNR, SSIM, VIF indicators have been improved, which verifies that the algorithm can achieve low-illumination palmprint image enhancement well.
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We would like to thank some institutions including Chinese Academy of Sciences, Indian Institute of Technology and Idiap Research Institute for providing the palmprint dataset.
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Zhou, K., Lu, D., Zhou, X., Liu, G. (2022). Low-illumination Palmprint Image Enhancement Based on U-Net Neural Network. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_55
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