Hand-dorsa vein recognition based on multi-level keypoint detection and local feature matching

As a new biometric for person authentication, hand-dorsa vein has attracted increasing attention in recent years. This paper proposes a novel approach for hand-dorsa vein recognition, which makes use of multi-level keypoint detection and SIFT feature based local matching. In order to overcome the difficulty in finding local features on NIR images of hand dorsa, a multi-level keypoint detection approach, composed by Harris-Laplace and Hessian-Laplace detectors, is designed to localize enough keypoints so that more discriminative information can be highlighted. Then SIFT based local matching efficiently associates these keypoints between hand dorsa of the same individual. The experimental results achieved on the NCUT database clearly indicate the effectiveness of the proposed method for hand-dorsa vein recognition.