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

Abstract

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.