Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition

On the basis of the increased emphasis placed on the protection of privacy, biometric recognition systems using physical or behavioural characteristics such as fingerprints, facial characteristics, iris and finger-vein patterns or the voice have been introduced in applications including door access...

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Main Authors: Young Ho Park, Kang Ryoung Park
Format: Article
Language:English
Published: SAGE Publishing 2012-10-01
Series:International Journal of Advanced Robotic Systems
Online Access:https://doi.org/10.5772/53474
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spelling doaj-bc1ba010a83c44daba4950ba24a38d382020-11-25T03:33:02ZengSAGE PublishingInternational Journal of Advanced Robotic Systems1729-88142012-10-01910.5772/5347410.5772_53474Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein RecognitionYoung Ho Park0Kang Ryoung Park1 Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of Korea Division of Electronics and Electrical Engineering, Dongguk University, Seoul, Republic of KoreaOn the basis of the increased emphasis placed on the protection of privacy, biometric recognition systems using physical or behavioural characteristics such as fingerprints, facial characteristics, iris and finger-vein patterns or the voice have been introduced in applications including door access control, personal certification, Internet banking and ATM machines. Among these, finger-vein recognition is advantageous in that it involves the use of inexpensive and small devices that are difficult to counterfeit. In general, finger-vein recognition systems capture images by using near infrared (NIR) illumination in conjunction with a camera. However, such systems can face operational difficulties, since the scattering of light from the skin can make capturing a clear image difficult. To solve this problem, we proposed new image quality enhancement method that measures the direction and thickness of vein lines. This effort represents novel research in four respects. First, since vein lines are detected in input images based on eight directional profiles of a grey image instead of binarized images, the detection error owing to the non-uniform illumination of the finger area can be reduced. Second, our method adaptively determines a Gabor filter for the optimal direction and width on the basis of the estimated direction and thickness of a detected vein line. Third, by applying this optimized Gabor filter, a clear vein image can be obtained. Finally, the further processing of the morphological operation is applied in the Gabor filtered image and the resulting image is combined with the original one, through which finger-vein image of a higher quality is obtained. Experimental results from application of our proposed image enhancement method show that the equal error rate (EER) of finger-vein recognition decreases to approximately 0.4% in the case of a local binary pattern-based recognition and to approximately 0.3% in the case of a wavelet transform-based recognition.https://doi.org/10.5772/53474
collection DOAJ
language English
format Article
sources DOAJ
author Young Ho Park
Kang Ryoung Park
spellingShingle Young Ho Park
Kang Ryoung Park
Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
International Journal of Advanced Robotic Systems
author_facet Young Ho Park
Kang Ryoung Park
author_sort Young Ho Park
title Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
title_short Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
title_full Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
title_fullStr Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
title_full_unstemmed Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition
title_sort image quality enhancement using the direction and thickness of vein lines for finger-vein recognition
publisher SAGE Publishing
series International Journal of Advanced Robotic Systems
issn 1729-8814
publishDate 2012-10-01
description On the basis of the increased emphasis placed on the protection of privacy, biometric recognition systems using physical or behavioural characteristics such as fingerprints, facial characteristics, iris and finger-vein patterns or the voice have been introduced in applications including door access control, personal certification, Internet banking and ATM machines. Among these, finger-vein recognition is advantageous in that it involves the use of inexpensive and small devices that are difficult to counterfeit. In general, finger-vein recognition systems capture images by using near infrared (NIR) illumination in conjunction with a camera. However, such systems can face operational difficulties, since the scattering of light from the skin can make capturing a clear image difficult. To solve this problem, we proposed new image quality enhancement method that measures the direction and thickness of vein lines. This effort represents novel research in four respects. First, since vein lines are detected in input images based on eight directional profiles of a grey image instead of binarized images, the detection error owing to the non-uniform illumination of the finger area can be reduced. Second, our method adaptively determines a Gabor filter for the optimal direction and width on the basis of the estimated direction and thickness of a detected vein line. Third, by applying this optimized Gabor filter, a clear vein image can be obtained. Finally, the further processing of the morphological operation is applied in the Gabor filtered image and the resulting image is combined with the original one, through which finger-vein image of a higher quality is obtained. Experimental results from application of our proposed image enhancement method show that the equal error rate (EER) of finger-vein recognition decreases to approximately 0.4% in the case of a local binary pattern-based recognition and to approximately 0.3% in the case of a wavelet transform-based recognition.
url https://doi.org/10.5772/53474
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