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Computer Science > Computer Vision and Pattern Recognition

arXiv:2112.07931 (cs)
[Submitted on 15 Dec 2021]

Title:From Noise to Feature: Exploiting Intensity Distribution as a Novel Soft Biometric Trait for Finger Vein Recognition

Authors:Wenxiong Kang, Yuting Lu, Dejian Li, Wei Jia
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Abstract:Most finger vein feature extraction algorithms achieve satisfactory performance due to their texture representation abilities, despite simultaneously ignoring the intensity distribution that is formed by the finger tissue, and in some cases, processing it as background noise. In this paper, we exploit this kind of noise as a novel soft biometric trait for achieving better finger vein recognition performance. First, a detailed analysis of the finger vein imaging principle and the characteristics of the image are presented to show that the intensity distribution that is formed by the finger tissue in the background can be extracted as a soft biometric trait for recognition. Then, two finger vein background layer extraction algorithms and three soft biometric trait extraction algorithms are proposed for intensity distribution feature extraction. Finally, a hybrid matching strategy is proposed to solve the issue of dimension difference between the primary and soft biometric traits on the score level. A series of rigorous contrast experiments on three open-access databases demonstrates that our proposed method is feasible and effective for finger vein recognition.
Comments: 11 pages
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2112.07931 [cs.CV]
  (or arXiv:2112.07931v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2112.07931
Journal reference: IEEE transactions on information forensics and security 14.4 (2018): 858-869
Related DOI: https://doi.org/10.1109/TIFS.2018.2866330

Submission history

From: Dejian Li [view email]
[v1] Wed, 15 Dec 2021 07:23:21 UTC (1,485 KB)
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