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3D ear recognition using local salience and principal manifold

Published: 01 September 2014 Publication History

Abstract

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.

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Published In

cover image Graphical Models
Graphical Models  Volume 76, Issue 5
September, 2014
356 pages

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Academic Press Professional, Inc.

United States

Publication History

Published: 01 September 2014

Author Tags

  1. Ear recognition
  2. Feature matching
  3. Point cloud
  4. Principal manifold
  5. Salient keypoint

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