Tri-Branch Vein Structure Assisted Finger Vein Recognition

Yang, Gongping; Yang, Geng; Xi, Xiaoming; Meng, Xianjing; Zhang, Chunyun; Yin, Yilong

doi:10.1109/access.2017.2728797

Cited by 44 publications

(34 citation statements)

References 27 publications

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“…In this section, the five parameters of the Gabor function can be learned and updated. And the performance of our method, compared with the state-of-the-art finger vein recognition algorithms on four public finger vein databases, is presented in Tables 3-6. As we can see from Tables 3-5, we compare our proposed adaptive Gabor filter method with other Gabor methods, such as Gabor filter, Gabor+Tri-branch structure [19], PG-Gabor [20], our method has better performance than other Gabor methods. This proves that our method can update the parameters of Gabor function better and extract the main vein information of finger vein image.…”

Section: Comparison With the State-of-the-art Algorithmsmentioning

confidence: 97%

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Adaptive Learning Gabor Filter for Finger-Vein Recognition

Zhang

et al. 2019

IEEE Access

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Presently, finger-vein recognition is a new research direction in the field of biometric recognition. The Gabor filter has been extensively used for finger-vein recognition; however, its parameters are difficult to adjust. To solve this problem, an adaptive-learning Gabor filter is presented herein. We combine convolutional neural networks with a Gabor filter to calculate the gradient of the Gabor-filter parameters, based on the objective function, and to then optimize its parameters via back-propagation. The parameter θ of Gabor filter can be trained to the same angle as the vein texture of finger vein image. The parameter σ of Gabor filter has a certain relation with λ, and the parameter λ of Gabor filter can converge to the optimal value well. Using this method, we not only select appropriate and effective Gabor filter parameters to design the filter banks, we also consider the relationship between those parameters. Finally, we perform experiments on four public finger-vein datasets. Experimental results demonstrate that our method outperforms state-ofthe-art methods in finger-vein classification.

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Section: Comparison With the State-of-the-art Algorithmsmentioning

confidence: 97%

“…Lu et al [18] proposed a feature extraction method based on Gabor-corresponding histograms. Yang et al proposed the Gabor+Tri-branch structure [19] and point group method combined with a Gabor filter (PG-Gabor) [20] to utilize the vein point and non-vein point. These methods solve different problems in finger-vein recognition.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Zhang

et al. 2019

IEEE Access

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“…Techniques for RoI determination are typically described in the context of descriptions of the entire recognition toolchain. There are hardly papers dedicated to this [76,94,163,203,209,299] Binary vascular structure using semantic segmentation CNNs [91,[100][101][102] Minutiae [84,148,293] issue separately. A typical example is [287], where an inter-phalangeal joint prior is used for finger vein RoI localisation and haze removal methods with the subsequent application of Gabor filters are used for improving visibility of the vascular structure.…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

“…Subsequent work uses vein minutiae (vessel bifurcations and endings) to represent the vascular structure. In [293], it is proposed to extract each bifurcation point and its local vein branches, named tri-branch vein structure, from the vascular pattern. As these features are particularly well suited to identify imposter mismatches, these are used as first stage in a serial fusion before conducting a second comparison stage using the entire vascular structure.…”

Section: Finger Vein Recognition Toolchainmentioning

confidence: 99%

State of the Art in Vascular Biometrics

Uhl

2019

Handbook of Vascular Biometrics

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The investigation of vascular biometric traits has become increasingly popular during the last years. This book chapter provides a comprehensive discussion of the respective state of the art, covering hand-oriented techniques (finger vein, palm vein, (dorsal) hand vein and wrist vein recognition) as well as eye-oriented techniques (retina and sclera recognition). We discuss commercial sensors and systems, major algorithmic approaches in the recognition toolchain, available datasets, public competitions and open-source software, template protection schemes, presentation attack(s) (detection), sample quality assessment, mobile acquisition and acquisition on the move, and finally eventual disease impact on recognition and template privacy issues.

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“…S SUPERPIXEL segmentation aims to obtain local regions with appearance and location consistency. It is used to extract perceptually meaningful element regions, which significantly reduces the computation complexity for other computer vision applications, such as saliency detection [1], [2], object segmentation [3]- [7], object detection [8] and recognition [9], and biomedical image analysis [10].…”

Section: Introductionmentioning

confidence: 99%

Superpixel Segmentation by Forgetting Geodesic Distance

2020

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Superpixel segmentation could be of benefit to computer vision tasks due to its perceptually meaningful results with similar appearance and location. To obtain the accurate superpixel segmentation, existing methods introduce geodesic distance to fit the object boundaries. However, conventional geodesic distance easily suffers from error accumulation and excessive time consumption. This paper proposes a fast superpixel segmentation method based on a new geodesic distance, called forgetting geodesic distance. In contrast to the conventional geodesic distance, the forgetting geodesic distance utilizes a forgetting factor to gradually reduce the influence of previous path cost and focuses on the latest pixels' difference. Intuitively, a pixel with lower difference with respect to the latest path contextual distance will be more similar with the corresponding region. In the new path, the path cost devotes much greater attention to the latest pixels' difference and could significantly relieve error accumulation. The pixels are also aggregated with less dependence on seeds as the path extends, which avoids the seed updating. The experimental results validate that the proposed method obtains 2 percent and 1 percent gain on average compared with most of the state-of-the-art methods in terms of BSD500 and VOC2012 datasets, respectively.

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Tri-Branch Vein Structure Assisted Finger Vein Recognition

Cited by 44 publications

References 27 publications

Adaptive Learning Gabor Filter for Finger-Vein Recognition

Adaptive Learning Gabor Filter for Finger-Vein Recognition

State of the Art in Vascular Biometrics

Superpixel Segmentation by Forgetting Geodesic Distance

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