An efficient 3-D ear recognition system employing local and holistic features

Jindan Zhou, Steven Cadavid, Mohamed Abdel-Mottaleb

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


We present a complete three-dimensional (3-D) ear recognition system combining local and holistic features in a computationally efficient manner. The system is comprised of four primary components, namely: 1) ear image segmentation; 2) local feature extraction and matching; 3) holistic feature extraction and matching; and 4) a fusion framework combining local and holistic features at the match score level. For the segmentation component, we introduce a novel shape-based feature set, termed the Histograms of Indexed Shapes (HIS), to localize a rectangular region containing the ear. For the local feature extraction and representation component, we extend the HIS feature descriptor to an object-centered 3-D shape descriptor, the Surface Patch Histogram of Indexed Shapes (SPHIS), for local ear surface representation and matching. For the holistic matching component, we introduce a voxelization scheme for holistic ear representation from which an efficient, voxel-wise comparison of gallery-probe model pairs can be made. The match scores obtained from both the local and holistic matching components are fused to generate the final match scores. Experimental results conducted on the University of Notre Dame (UND) collection G dataset, containing range images of 415 subjects yielded a rank-one recognition rate of 98.3% and an equal error rate of 1.7%. These results demonstrate that the proposed approach outperforms state-of-the-art 3-D ear biometric systems. Additionally, the method is considerably more efficient compared to the state-of-the-art because it employs a sparse set of features rather than using the dense model.

Original languageEnglish (US)
Article number6158598
Pages (from-to)978-991
Number of pages14
JournalIEEE Transactions on Information Forensics and Security
Issue number3
StatePublished - 2012


  • 3-D ear recognition
  • 3-D ear segmentation
  • Ear biometrics
  • Range image
  • Shape index
  • Surface matching
  • Voxelization

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Safety, Risk, Reliability and Quality


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