Feature extraction from vein images using spatial information and chain codes

The pattern formed by subcutaneous blood vessels is unique attribute of each individual and can therefore be used as a biometric characteristic. Exploiting the specific near infrared light absorption properties of blood, the capture procedure for this biometric characteristic is convenient and allows contact-less sensors. However, image skeletons extracted from vein images are often unstable, because the raw vein images suffer from low contrast. We propose a new chain code based feature encoding method, using spatial and orientation properties of vein patterns, which is capable of dealing with noisy and unstable image skeletons. Chain code comparison and a selection of preprocessing methods have been evaluated in a series of different experiments in single and multireference scenarios on two different vein image databases. The experiments showed that chain code comparison outperforms minutiae-based approaches and similarity based mix matching.