期刊
GRAPHICAL MODELS
卷 76, 期 -, 页码 402-412出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.gmod.2014.03.003
关键词
Ear recognition; Salient keypoint; Principal manifold; Point cloud; Feature matching
资金
- National Natural Science Foundation of China [61170143, 60873110, 61370141]
As an emerging class of biometrics, human ear has drawn significant attention in recent years. In this paper, we propose a novel 3D ear shape matching and recognition system. First, we propose a novel method for computing saliency value of each point on 3D ear point clouds, which is based on the Gaussian-weighted average of the mean curvature and can be used to sort the keypoints accordingly. Then we propose the optimal selection of the salient key points using the Poisson Disk Sampling. Finally, we fit a surface to the neighborhood of each salient keypoint using the quadratic principal manifold method, establishing the local feature descriptor of each salient keypoint. The experimental results on ear shape matching show that, compared with other similar methods, the proposed system has higher approximation precision on shape feature detection and higher matching accuracy on the ear recognition. (C) 2014 Elsevier Inc. All rights reserved.
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