Superpixel based finger vein ROI extraction with sensor interoperability

Yang, Gongping; Zhou, Lizhen; Yin, Yilong

doi:10.1109/icb.2015.7139108

Cited by 29 publications

(15 citation statements)

References 18 publications

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“…or using a mask-based edge detection template. In [ 38 ], superpixel segmentation (by using simple linear iterative clustering (SLIC) [ 39 ]) and the Sobel edge detector were combined to determine accurate finger boundary lines. The finger ROI was localized by the internal tangents of finger boundaries.…”

Section: Related Workmentioning

confidence: 99%

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Yao

Song

2020

Sensors

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Region of interest (ROI) localization is one of the key preprocessing technologies for a finger-vein identification system, so an effective ROI definition can improve the matching accuracy. However, due to the impact of uneven illumination, equipment noise, as well as the distortion of finger position, etc., these make accurate ROI localization a very difficult task. To address these issues, in this paper, we propose a robust finger-vein ROI localization method, which is based on the 3 σ criterion dynamic threshold strategy. The proposed method includes three main steps: First, the Kirsch edge detector is introduced to detect the horizontal-like edges in the acquired finger-vein image. Then, the obtained edge gradient image is divided into four parts: upper-left, upper-right, lower-left, and lower-right. For each part of the image, the three-level dynamic threshold, which is based on the 3 σ criterion of the normal distribution, is imposed to obtain more distinct and complete edge information. Finally, through labeling the longest connected component at the same horizontal line, two reliable finger boundaries, which represent the upper and lower boundaries, respectively, are defined, and the ROI is localized in the region between these two boundaries. Extensive experiments are carried out on four different finger-vein image datasets, including three publicly available datasets and one of our newly developed finger-vein datasets with 37,080 finger-vein samples and 1030 individuals. The experimental results indicate that our proposed method has very competitive ROI localization performance, as well as satisfactory matching results on different datasets.

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Section: Related Workmentioning

confidence: 99%

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Yao

Song

2020

Sensors

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“…Each of 636 fingers has 6 images. Each image has 320×240 pixels, and our prior image preprocessing method [31] is used in finger segmentation. The preprocessed image has the same size with the original image.…”

Section: A Databasesmentioning

confidence: 99%

“…All images are 8-bit gray level BMP files with a resolution of 640×480 pixels. Finger region in a raw image is labeled by the preprocessing method in [31].…”

Section: A Databasesmentioning

confidence: 99%

Point Grouping Method for Finger Vein Recognition

Yang

Wang

et al. 2019

IEEE Access

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In finger vein recognition, vein points-based methods classified the image points into vein points and non-vein points and only measured the vein points for the recognition. All points-based methods utilized the features of all points regardless of the difference of vein points and non-vein points. This paper proposes a point grouping method for finger vein recognition by integrating the advantages of the above-mentioned two kinds of methods. In the proposed method, all image points are considered in the recognition, and the points are classified into multiple groups in both feature extraction and similarity measurement. The matched (unmatched) points are separately found from each group pair of the enrolled image and the probe and are fused to obtain the similarity (dissimilarity) score. Moreover, we incorporate the idea of the point grouping into two popular finger vein recognition methods and devise the corresponding point grouping assisted methods, namely, point grouping assisted anatomy structure analysis-based vein extraction and point grouping assisted Gabor. The experimental results on finger vein databases demonstrate the efficacy of the point grouping assisted methods. The equal error rate values of our point grouping methods are lower about 1% than their base methods on SDUD in the multiple templates' mode and on HKPUD in the single template mode.

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“…FVC 2000 and DBIT fingerprint databases are used to evaluate the algorithm and it achieves the average efficiency of 82.95 % and 89.68% respectively. Lu Yang, Gongping Yang, Lizhen Zhou, Yilong Yin proposed a super pixel based finger vein region of interest (ROI) extraction with sensor interoperability in biometric systems 19 . Finger boundaries were firstly determined by tracking super pixels.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Address Sensor Interoperability Using Gabor Filter

2017

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Biometric authentication using fingerprint is one of the unique and reliable method of verification processes. Biometric System suffers a signiûcant loss of performance when the sensor is changed during enrollment and authentication process. In this paper fingerprint sensor interoperability problem is addressed using Gabor ûlter and classifying images into good and poor quality. Gabor ûlters play an important role in many application areas for the enhancement of various types of fingerprint images. Gabor ûlters can remove noise, preserve the real ridges and valley structures, and it is used for fingerprint image enhancement. Experimental results on the FVC2004 databases show improvements of this approach.

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Superpixel based finger vein ROI extraction with sensor interoperability

Cited by 29 publications

References 18 publications

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Robust Finger-vein ROI Localization Based on the 3σ Criterion Dynamic Threshold Strategy

Point Grouping Method for Finger Vein Recognition

A Novel Approach to Address Sensor Interoperability Using Gabor Filter

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