2021
DOI: 10.3390/s21051885
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A Novel Finger Vein Recognition Method Based on Aggregation of Radon-Like Features

Abstract: Finger vein (FV) biometrics is one of the most promising individual recognition traits, which has the capabilities of uniqueness, anti-forgery, and bio-assay, etc. However, due to the restricts of imaging environments, the acquired FV images are easily degraded to low-contrast, blur, as well as serious noise disturbance. Therefore, how to extract more efficient and robust features from these low-quality FV images, remains to be addressed. In this paper, a novel feature extraction method of FV images is present… Show more

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Cited by 14 publications
(7 citation statements)
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“…Here, we adopted a specially designed concatenation strategy. Specifically, supposing the output of each GRN branch is an n-dimensional vector (in our GRNs, n was equal to 200), the concatenation of these two n-dimensional vectors is performed by using Equation (5).…”
Section: Framework Of Siamese Gabor Residual Networkmentioning
confidence: 99%
See 2 more Smart Citations
“…Here, we adopted a specially designed concatenation strategy. Specifically, supposing the output of each GRN branch is an n-dimensional vector (in our GRNs, n was equal to 200), the concatenation of these two n-dimensional vectors is performed by using Equation (5).…”
Section: Framework Of Siamese Gabor Residual Networkmentioning
confidence: 99%
“…In the early stages of research, some meticulously hand-crafted features were adopted by FV verification systems. One kind of method, namely "vein-level" [5], was devoted to characterizing the geometric and topological shapes of the vein network (such as point-shaped [6][7][8][9], line-shaped [10,11], curve-based [12][13][14], etc.). In addition, anatomical structures [15,16] and even vein pulsation patterns [17] were also introduced for feature representation.…”
Section: Introductionmentioning
confidence: 99%
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“…But the approach failed to enhance the accuracy without increasing the time consumption [17]. To improve recognition accuracy, [18] introduced a novel feature extraction technique for finger vein verification. But, the image de-noising was not performed in a way to minimize the time complexity of FVV.…”
Section: Related Workmentioning
confidence: 99%
“…In contrast, biometric systems use intrinsic characteristics such as finger veins and palm veins that are hidden beneath the skin, which are difficult to forge [5][6][7]. Particularly, finger vein authentication systems have demonstrated promising applications in consumer electronics, banking, and airports [8].…”
Section: Introductionmentioning
confidence: 99%