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3D Ear Based Human Recognition Using Gauss Map Clustering

Published: 16 November 2017 Publication History

Abstract

This paper addresses the problem of human recognition using 3D ear biometrics. Existing feature extraction and description techniques in the literature for 3D shape recognition works well with the different class of shapes, however, not for profoundly comparable objects like human 3D ears. This work proposes an effective method utilizing Gauss mapping for feature keypoints detection and shape context to describe the detected keypoints. The proposed technique is as follows. A triangle for every point p is computed using two other points of the k-nearest neighbors within a sphere of radius r. A normal is computed for the obtained triangle and is mapped to a unit sphere. This mapping of normals is done for every conceivable triangle of point p. It is observed that mapped normals form a different number of clusters depending upon the type of surface point p belongs to. A point is considered as a keypoint if its projected normals form more than two clusters. Further, we project all the detected keypoints onto a plane and use them in the computation of feature descriptor vectors. Descriptor vector of a keypoint is computed by keeping it at the center and defining its shape context considering all other keypoints as its neighbors. To match a probe ear image with a gallery image for recognition, we compute correspondence for all the feature keypoints of the probe image to the feature keypoints of the gallery image. Final matching is performed by aligning the gallery image with the probe image and considering the registration error as the matching score. The experimental analysis conducted on University of Notre Dame (UND)-Collection J2 has achieved a verification accuracy of 98.20% with an equal error rate (EER) of 1.84%.

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cover image ACM Other conferences
Compute '17: Proceedings of the 10th Annual ACM India Compute Conference
November 2017
148 pages
ISBN:9781450353236
DOI:10.1145/3140107
© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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  • ACM India: ACM India

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 November 2017

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Author Tags

  1. 3D Ear
  2. Biometrics
  3. Ear recognition
  4. Feature Descriptor
  5. Gauss Mapping
  6. Local Feature keypoints
  7. person verification/ identification

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • Science & Engineering Research Board (SERB), DST, Govt of India

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Compute '17
Compute '17: ACM Compute 2017
November 16 - 18, 2017
Bhopal, India

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Compute '17 Paper Acceptance Rate 19 of 70 submissions, 27%;
Overall Acceptance Rate 114 of 622 submissions, 18%

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