Muliscale Vessel Enhancement Filtering
MICCAI '98 Proceedings of the First International Conference on Medical Image Computing and Computer-Assisted Intervention
Machine Vision and Applications
Extraction of Finger-Vein Patterns Using Maximum Curvature Points in Image Profiles
IEICE - Transactions on Information and Systems
Personal authentication using finger vein pattern and finger-dorsa texture fusion
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Vein segmentation in infrared images using compound enhancing and crisp clustering
ICVS'08 Proceedings of the 6th international conference on Computer vision systems
Efficient Finger Vein Localization and Recognition
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Capturing Hand or Wrist Vein Images for Biometric Authentication Using Low-Cost Devices
IIH-MSP '10 Proceedings of the 2010 Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing
A thermal hand vein pattern verification system
ICAPR'05 Proceedings of the Third international conference on Pattern Recognition and Image Analysis - Volume Part II
IEEE Transactions on Image Processing
Spectral minutiae for vein pattern recognition
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
Palm vein recognition with Local Binary Patterns and Local Derivative Patterns
IJCB '11 Proceedings of the 2011 International Joint Conference on Biometrics
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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 en- coding 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 multi-reference scenarios on two different vein image databases. The experiments showed that chain code comparison outperforms minutiae-based approaches and similarity based mix matching.