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Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex.

Xie SJ, Lu Y, Yoon S, Yang J, Park DS - Sensors (Basel) (2015)

Bottom Line: However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person.This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs).The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

View Article: PubMed Central - PubMed

Affiliation: Institute of Remote Sensing and Earth Science, College of Science, Hangzhou Normal University, Hangzhou 311121, China. shanj_x@hotmail.com.

ABSTRACT
Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

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Two groups of images, (a) and (b), and their ROIs from UTFVP [22].
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sensors-15-17089-f008: Two groups of images, (a) and (b), and their ROIs from UTFVP [22].

Mentions: UTFVP [22] contains 1440 finger vascular pattern images in total which have been collected from 60 volunteer at the University of Twente. Images were captured in two identical sessions with an average time lapse of 15 days. The vascular pattern of the index, ring and middle finger of both hands has been collected twice at each session. Two images were collected for each finger in each session. The captured images have a resolution of 672 × 380. Each image is stored using the lossless 8-bit grey scale PNG format. ROIs of images in UTFVP, extracted using the algorithm proposed in [27], had the resolution of 60 × 120. Some of the finger vascular pattern images from UTFVP and their ROIs are displayed in Figure 8.


Intensity Variation Normalization for Finger Vein Recognition Using Guided Filter Based Singe Scale Retinex.

Xie SJ, Lu Y, Yoon S, Yang J, Park DS - Sensors (Basel) (2015)

Two groups of images, (a) and (b), and their ROIs from UTFVP [22].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541924&req=5

sensors-15-17089-f008: Two groups of images, (a) and (b), and their ROIs from UTFVP [22].
Mentions: UTFVP [22] contains 1440 finger vascular pattern images in total which have been collected from 60 volunteer at the University of Twente. Images were captured in two identical sessions with an average time lapse of 15 days. The vascular pattern of the index, ring and middle finger of both hands has been collected twice at each session. Two images were collected for each finger in each session. The captured images have a resolution of 672 × 380. Each image is stored using the lossless 8-bit grey scale PNG format. ROIs of images in UTFVP, extracted using the algorithm proposed in [27], had the resolution of 60 × 120. Some of the finger vascular pattern images from UTFVP and their ROIs are displayed in Figure 8.

Bottom Line: However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person.This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs).The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

View Article: PubMed Central - PubMed

Affiliation: Institute of Remote Sensing and Earth Science, College of Science, Hangzhou Normal University, Hangzhou 311121, China. shanj_x@hotmail.com.

ABSTRACT
Finger vein recognition has been considered one of the most promising biometrics for personal authentication. However, the capacities and percentages of finger tissues (e.g., bone, muscle, ligament, water, fat, etc.) vary person by person. This usually causes poor quality of finger vein images, therefore degrading the performance of finger vein recognition systems (FVRSs). In this paper, the intrinsic factors of finger tissue causing poor quality of finger vein images are analyzed, and an intensity variation (IV) normalization method using guided filter based single scale retinex (GFSSR) is proposed for finger vein image enhancement. The experimental results on two public datasets demonstrate the effectiveness of the proposed method in enhancing the image quality and finger vein recognition accuracy.

Show MeSH