This paper presents a biometric identification system based on near-infrared imaging of dorsal hand veins and matching of the keypoints that are extracted from the dorsal hand vein images by the scale-invariant feature transform. The whole system is covered in detail, which includes the imaging device used, image processing methods proposed for geometric correction, region-of-interest extraction, image enhancement and vein pattern segmentation, as well as image classification by extraction and matching of keypoints. In addition to several constraints introduced to minimise incorrectly matched keypoints, a particular focus is placed on the use of multiple training images of each hand class to improve the recognition performance for a large database with more than 200 hand classes. By organising multiple keypoint sets extracted from multiple training images of each hand class into three sets, namely, the union, the intersection and the exclusion, based on their inter-class and intra-class relationships, this study shows the contribution made by each set to the recognition performance and demonstrates the feasibility of achieving 100% correct recognition by combining the three sets, based on the experiments conducted using more than 2000 dorsal hand vein images.

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Personal identification based on multiple keypoint sets of dorsal hand vein images

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