Automatic 3D facial region retrieval from multi-pose facial datasets

  • Authors:

  • P. Perakis;T. Theoharis;G. Passalis;I. A. Kakadiaris

  • Affiliations:

  • Computer Graphics Laboratory, Department of Informatics & Telecommunications, University of Athens, Greece and Computational Biomedicine Laboratory, Department of Computer Science, University ...;Computer Graphics Laboratory, Department of Informatics & Telecommunications, University of Athens, Greece and Computational Biomedicine Laboratory, Department of Computer Science, University ...;Computer Graphics Laboratory, Department of Informatics & Telecommunications, University of Athens, Greece and Computational Biomedicine Laboratory, Department of Computer Science, University ...;Computational Biomedicine Laboratory, Department of Computer Science, University of Houston, Texas

  • Venue:
  • EG 3DOR'09 Proceedings of the 2nd Eurographics conference on 3D Object Retrieval
  • Year:
  • 2009

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Abstract

The availability of 3D facial datasets is rapidly growing, mainly as a result of medical and biometric applications. These applications often require the retrieval of specific facial areas (such as the nasal region). The most crucial step in facial region retrieval is the detection of key 3D facial landmarks (e.g., the nose tip). A key advantage of 3D facial data over 2D facial data is their pose invariance. Any landmark detection method must therefore also be pose invariant. In this paper, we present the first 3D facial landmark detection method that works in datasets with pose rotations of up to 80 degree around the y-axis. It is tested on the largest publicly available 3D facial datasets, for which we have created a ground truth by manually annotating the 3D landmarks. Landmarks automatically detected by our method are then used to robustly retrieve facial regions from 3D facial datasets.