Dorsal Hand Vein Extraction in Uncontrolled Environment

Abstract

Biometrics is an inseparable part of our day to day life. A major development in this area has been observed in past few decades. Over the recent years, dorsal hand veins have emerged as a promising biometric attribute due to its universality, stability and anti-forgery characteristics. However, detecting the veins of different thickness under different illumination is a challenging task. The traditional vein extraction approaches based on thresholding does not find their applicability in these situations. This paper presents a hybrid approach for vein segmentation for these hand images. The proposed approach is a combination of two techniques, i.e. repeated line tracking and maximum curvature points. The technique has been tested over Bosphorus hand vein dataset which contains 1575 images of different age groups captured under different illumination conditions. From the results, it is evident that this technique is suitable to extract vein pattern from all types of images. Further, these images have yielded an accuracy of more than 98% when subjected to feature extraction and classification steps.