A hierarchical finger selection method for finger vein recognition using sift

Sujani, G.; Reddy, G. M. Sreerama

doi:10.1109/smarttechcon.2017.8358518

Cited by 2 publications

(3 citation statements)

References 9 publications

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“…From Table 1, we also observe that the baseline segmentation algorithm [30] achieve higher verification accuracy on database B compared to results on database A, but the proposed approach still improve accuracy. In addition, the LBP descriptor [34] and SIFT descriptor [35] do not achieve promising verification accuracy on two public databases, which can be explained that the finger-vein image contains less local feature points because vein patterns are non-rigid, round and smooth objects.…”

Section: E Verification Resultsmentioning

confidence: 96%

“…The EER of the system [30] before and after automatically restoring the vein patterns is calculated to get insight on finger-vein verification. In addition, as two descriptors of LBP [34] and SIFT [35] impostor matching scores are produced from dataset B3.The verification results for database A and database B are listed in Table 1 and corresponding ROC curve is showed in Fig.14. We observe from Fig.14 and Table 1 that the proposed approach using a GAN deep representation achieves the best performance among all the approaches considered in this work.…”

Section: E Verification Resultsmentioning

confidence: 99%

“…In addition, the method can be used for pattern restoration of other images, such as fingerprints and palm-print images. The proposed restoration approach The restoration approach [25] The restoration approach [12] The segmentation algorithm [30] LBP [35] SIFT [34] The proposed restoration approach The restoration approach [25] The restoration approach [12] The segmentation algorithm [30] LBP [35] SIFT [34]…”

Section: Discussionmentioning

confidence: 99%

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Finger-Vein Pattern Restoration With Generative Adversarial Network

et al. 2020

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Finger-vein recognition technology has attracted more and more attention because of its high security and convenience. However, the finger-vein image capturing is affected by various factors, which results that some vein patterns are missed in acquired image. Matching minutiae features in such images ultimately degrades verification performance of the finger-vein recognition system. To overcome this problem, in this paper, a novel finger-vein image restoration approach is proposed to recover the missed patterns based on generative adversarial network (GAN), as the first attempt in this area. Firstly, we employ the segmentation algorithm to extract finger-vein network, which is further subject to thinning operation. Secondly, the resulting thinning image is taken as an input of a GAN model to restore the missed vein patterns. Thirdly, the minutiae points are extracted from restoration finger-vein pattern. Finally, we propose a matching approach for verification. Experimental results show that the proposed method can restore the missed vein pattern and reduce the equal error rate (EER) of the finger-vein verification system. INDEX TERMS Finger-vein recognition, vein restoration, generative adversarial network.

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Section: E Verification Resultsmentioning

confidence: 96%

Section: E Verification Resultsmentioning

confidence: 99%