3-D ear modeling and recognition from video sequences using shape from shading

Steven Cadavid, Mohamed Abdel-Mottaleb

Research output: Contribution to journalArticlepeer-review

39 Scopus citations


We describe a novel approach for 3-D ear biometrics using video. A series of frames is extracted from a video clip and the region of interest in each frame is independently reconstructed in 3-D using shape from shading. The resulting 3-D models are then registered using the iterative closest point algorithm. We iteratively consider each model in the series as a reference model and calculate the similarity between the reference model and every model in the series using a similarity cost function. Cross validation is performed to assess the relative fidelity of each 3-D model. The model that demonstrates the greatest overall similarity is determined to be the most stable 3-D model and is subsequently enrolled in the database. Experiments are conducted using a gallery set of 402 video clips and a probe of 60 video clips. The results (95.0% rank-1 recognition rate and 3.3% equal error rate) indicate that the proposed approach can produce recognition rates comparable to systems that use 3-D range data. To the best of our knowledge, we are the first to develop a 3-D ear biometric system that obtains a 3-D ear structure from a video sequence.

Original languageEnglish (US)
Article number4668367
Pages (from-to)709-718
Number of pages10
JournalIEEE Transactions on Information Forensics and Security
Issue number4
StatePublished - Dec 2008


  • 3-D ear biometrics
  • 3-D ear identification
  • 3-D ear modeling
  • 3-D ear verification
  • Shape from shading (SFS)

ASJC Scopus subject areas

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


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