Abstract
Biometrics is a technology that enables an individual person to be identified based on human physiological and behavioral characteristics. Among biometrics technologies, face recognition has been widely used because of its advantages in terms of convenience and non-contact operation. However, its performance is affected by factors such as variation in the illumination, facial expression, and head pose. Therefore, fingerprint and iris recognitions are preferred alternatives. However, the performance of the former can be adversely affected by the skin condition, including scarring and dryness. In addition, the latter has the disadvantages of high cost, large system size, and inconvenience to the user, who has to align their eyes with the iris camera. In an attempt to overcome these problems, finger-vein recognition has been vigorously researched, but an analysis of its accuracies according to various factors has not received much attention. Therefore, we propose a nonintrusive finger-vein recognition system using a near infrared (NIR) image sensor and analyze its accuracies considering various factors. The experimental results obtained with three databases showed that our system can be operated in real applications with high accuracy; and the dissimilarity of the finger-veins of different people is larger than that of the finger types and hands.
Highlights
Recent developments have led to the widespread use of biometric technologies, such as face, fingerprint, vein, iris, and voice recognition, in a variety of applications in access control, financial transactions on mobile devices, and automatic teller machines (ATMs) [1,2,3,4]
This device consists of six near infrared (NIR) light-emitting diodes (LEDs) operating at a wavelength of 850 nm and a webcam (Logitech Webcam C600) [26]
From the image consisting of 50 × 20 pixels, obtained as a result of sub-sampling, we obtained the average image from which we extracted the local binary pattern (LBP) code which was enrolled into the system
Summary
Recent developments have led to the widespread use of biometric technologies, such as face, fingerprint, vein, iris, and voice recognition, in a variety of applications in access control, financial transactions on mobile devices, and automatic teller machines (ATMs) [1,2,3,4]. Finger-vein recognition has been highlighted because it can overcome several drawbacks of other biometric technologies, such as the effect of sweat, skin distortions, and scars in fingerprint recognition, or the effect of poses and illumination changes in face recognition. Previous work on finger-vein recognition include research aimed at enhancing vein image quality, increasing recognition accuracy by various feature extraction methods, considering finger veins as a factor for individual recognition in multimodal systems, as well as detecting fake finger veins. The restoration-based methods proposed by Yang et al [9,10,11] were able to produce enhanced finger-vein images by considering the effect of the layered structure of skin and restored the images by using a point-spread function (PSF)
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