TY - JOUR
T1 - Deep learning-based restoration of multi-degraded finger-vein image by non-uniform illumination and noise
AU - Hong, Jin Seong
AU - Kim, Seung Gu
AU - Kim, Jung Soo
AU - Park, Kang Ryoung
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/7
Y1 - 2024/7
N2 - 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.
AB - 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.
KW - Deep learning
KW - Finger-vein recognition
KW - Generative adversarial network
KW - Multiple degradation factors
KW - Non-uniform illumination and noise
UR - http://www.scopus.com/inward/record.url?scp=85185528521&partnerID=8YFLogxK
U2 - 10.1016/j.engappai.2024.108036
DO - 10.1016/j.engappai.2024.108036
M3 - Article
AN - SCOPUS:85185528521
SN - 0952-1976
VL - 133
JO - Engineering Applications of Artificial Intelligence
JF - Engineering Applications of Artificial Intelligence
M1 - 108036
ER -