New Insights on Finger Knuckle Print Identification with Steerable Filter for Faster and Accurate Matching

Kavipriya, A.; Muthukumar, A.

doi:10.1109/incos45849.2019.8951414

Cited by 1 publication

(2 citation statements)

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“…Based on K matching pairs, the K sub-regions ′ and ′ from A and B are denoted as follows. (11) Where p and q are the translation in the horizontal and vertical directions, respectively, and Φ is defined as follows.…”

Section: ) Template Matching Based On Hausdorffmentioning

confidence: 99%

“…For fingerprint recognition and finger knuckle recognition [9], [10], [11], [12], the minutiae features such as bifurcation points and ending points have been employed as discriminative features and successfully applied for recognition because they have the advantage of low storage cost, low matching time cost and distinction. Similarly, the minutiae features of finger-vein patterns including bifurcation points and ending points as the common features are extracted for finger-vein recognition [13], [14], [15], 16] and showed promising performance.…”

Section: Introductionmentioning

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: ) Template Matching Based On Hausdorffmentioning

confidence: 99%