Deep learning-based restoration of multi-degraded finger-vein image by non-uniform illumination and noise

The recognition performance deteriorates if degradation factors including blur, noise, and non-uniform illumination exist in the image when acquiring a finger-vein image. Especially, multiple degradation factors can occur when acquiring the finger-vein image, and they require the image restoration. However, previous flow-based model produced lower image quality than the other restoration models, and diffusion-based model had the disadvantage of slow inference speed. Therefore, this study suggests a deep learning-based generative adversarial network for multi-degraded finger-vein image restoration by non-uniform illumination and noise (MFNN-GAN). It considers multiple degradation factors such as non-uniform illumination and noise. Unlike the existing finger-vein image restoration model, MFNN-GAN is capable of adaptive restoration to multiple degradations. Therefore, even if the illumination by near-infrared (NIR) illuminator of finger-vein recognition device is weak or non-uniform, or the consequent captured image is noisy, good recognition performance can be achieved only by our method without replacing the illuminator or camera sensor. The experimental results obtained using finger-vein open datasets, session 1 images from database version 1 of the Hong Kong Polytechnic University finger-image (HKPU-DB) and finger-vein database of SDUMLA-HMT (SDUMLA-HMT-DB)-based degraded databases. The experimental results show that we obtained the lower equal error rate (EER) of finger-vein recognition using MFNN-GAN compared to other state-of-the-art algorithms.