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Palm vein recognition is a new biometric identification technology. The horizontal rotation, translation and tilting of palm vein image greatly affect recognition rate. To solve the above problems, this paper proposed a recognition method for palm vein based on affine geometric properties. Firstly, the palm area of the palm vein image is obtained through image preprocessing. Secondly, a series of centroids of palm and segments are extracted. Feature vectors are constructed with the area radio of the triangles which are formed with centroids. Finally, Euclidean distance is used as the matching criteria. Experimental results show that the proposed method can obtain high recognition ratio and is robust to rotation and tilt of vein image. [ABSTRACT FROM PUBLISHER]

This paper presents a new keypoint-based approach for Near-Duplicate (ND) keyframes identification using global and local features. In the proposed method, the matched keypoints are filtered for affine transform estimation based on an affine invariant feature which is using the lesser matching pairs and voting bins. To further confirm the matching, the Local Binary Pattern (LBP) and color features of areas formed by matched keypoints in two images are compared. This method combines the advantage of global and local matching, it is able to identify two images related with affine transform. The proposed algorithm has been tested in Columbia dataset and conducted the quantitative comparison with RANSAC algorithm and SR-PE algorithm. The experiment results show that the proposed method can achieve good results. [ABSTRACT FROM PUBLISHER]