Static and dynamic analysis of near infra-red dorsal hand vein images for biometric applications

Abstract

This thesis presents the work carried out on static and dynamic analysis of near infra-red dorsal hand vein images for biometric applications. It focuses on the investigation of the classification of old and young groups of people, and analyses vital signs underlying dorsal hand vein images.
All research is based on the database used in this work. Focussing on the aged group of dorsal hand vein images, a database including static images and video images was first created. The static database contained 1000 images from 50 individuals and the dynamic database included 40 videos from 20 old participants.
For static analysis, dorsal hand vein images were pre-processed by geometry correction, regions of interest extraction (ROI), grey level normalisation, noise reduction and image enhancement. Then, skin and vein areas from the dorsal hand were segmented using maximum curvature based algorithms. Due to varying haemoglobin and water levels in the vein and skin, intensity based parameters were investigated and extracted as features for the classification of old and young groups. Two classifiers, linear discriminant analysis (LDA) and k-nearest neighbours (KNN) were adopted for comparative discussions. The experimental results turned out to be satisfactory and the two groups were well classified, using statistical intensity based features.
For dynamic analysis, mean grey levels were extracted from the ROI of each frame of the dorsal hand vein image videos. Then, all parameters were connected to form a biometric signal. The signal was analysed in a spectrum to detect a major peak for liveness. A fake dorsal hand video was introduced for comparative studies.
Experimental results showed that a respiratory like signal was detected as the main peak in the spectrum, verifying the vital signs of dynamic dorsal hand vein images and proposing a new method of liveness detection in biometric applications