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Abstract
Because of the advantages of finger-vein recognition systems such as live detection and usage as bio-cryptography systems, they can be used to authenticate individual people. However, images of finger-vein patterns are typically unclear because of light scattering by the skin, optical blurring, and motion blurring, which can degrade the performance of finger-vein recognition systems. In response to these issues, a new enhancement method for finger-vein images is proposed. Our method is novel compared with previous approaches in four respects. First, the local and global features of the vein lines of an input image are amplified using Gabor filters in four directions and Retinex filtering, respectively. Second, the means and standard deviations in the local windows of the images produced after Gabor and Retinex filtering are used as inputs for the fuzzy rule and fuzzy membership function, respectively. Third, the optimal weights required to combine the two Gabor and Retinex filtered images are determined using a defuzzification method. Fourth, the use of a fuzzy-based method means that image enhancement does not require additional training data to determine the optimal weights. Experimental results using two finger-vein databases showed that the proposed method enhanced the accuracy of finger-vein recognition compared with previous methods.
Highlights
With the increased demand for personal information security, biometric technologies such as iris, face, fingerprint, finger-vein, voice, gait, palm-print, and hand geometry recognition have been employed in a wide number of security systems, e.g., building access, computer log-ins, door access control, cellular phones, and ATMs [1,2,3,4]
If the near infrared (NIR) illuminators were positioned at the sides of the finger, the camera could capture the finger-vein image while the finger is illuminated from the side
Gabor filtering and Retinex filtering with a sigma value of 20 based on an image from database I: (a) Gabor filtered image; (b) Retinex filtered image with a sigma value of 20; (c) Fuzzy first of maxima (FOM) based on the Min rule; (d) Fuzzy last of maxima (LOM) based on the Min rule; and (e) Retinex filtered image with a sigma value of 50
Summary
With the increased demand for personal information security, biometric technologies such as iris, face, fingerprint, finger-vein, voice, gait, palm-print, and hand geometry recognition have been employed in a wide number of security systems, e.g., building access, computer log-ins, door access control, cellular phones, and ATMs [1,2,3,4]. Finger-vein recognition systems are used to authenticate individuals as enrolled or non-enrolled, and it has various advantages, such as live detection and possible applications in bio-cryptography systems [5]. Finger-vein recognition uses the vein patterns detected inside the finger. The area of a finger-vein image can be separated into regions with vein and non-vein patterns. The vein patterns of all fingers of the same person have different characteristics. To facilitate higher recognition accuracy, some finger-vein recognition systems use more than two fingers from the same individual
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