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Robust 3D Face Recognition from Expression Categorisation

By Jamie A. Cook, Mark D. Cox, Vinod Chandran and Sridha Sridharan

Abstract

The task of Face Recognition is often cited as being complicated by the presence of lighting and expression variation. In this article a novel combination of facial expression categorisation and 3D Face Recognition is used to provide enhanced recognition performance. The use of 3D face data alleviates performance issues related to pose and illumination. Part-face decomposition is combined with a novel adaptive weighting scheme to increase robustness to expression variation. By using local features instead of a monolithic approach, this system configuration allows for expression variability to be modelled and aid in the fusion process. The system is tested on the Face Recognition Grand Challenge (FRGC) database, currently the largest available dataset of 3D faces. The sensitivity of the proposed approach is also evaluated in the presence of systematic error in the expression classification stage

Topics: 080104 Computer Vision, 080101 Adaptive Agents and Intelligent Robotics, face recognition, expression, gabor, part face, FRGC
Publisher: Springer
Year: 2007
DOI identifier: 10.1007/978-3-540-74549-5_29
OAI identifier: oai:eprints.qut.edu.au:10913

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Citations

  1. (2006). A.: An integrated 3D face-expression recognition approach. In: Acoustics, Speech and Signal Processing,
  2. (2005). Adaptive rigid multi-region selection for handling expression variation in 3d face recognition. In: Computer Vision and Pattern Recognition,
  3. (2006). C.: 3D Face Recognition using Log-Gabor Templates.
  4. (1999). Classifying Facial Actions. Pattern Analysis and Machine Intelligence,
  5. (2006). Expression-Invariant Face Recognition with Expression Classi¯cation.
  6. (2005). Face recognition through mismatch driven representations of the face. In: Visual Surveillance and Performance Evaluation of Tracking and Surveillance,
  7. (2003). Face recognition: A literature survey.
  8. (1993). Face Recognition: Features versus Templates.
  9. (2002). Feature selection for pose invariant face recognition.
  10. (2003). Improved Facial-Feature Detection for AVSP via Unsupervised Clustering and Discriminant Analysis.
  11. (2006). P.: A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition.
  12. (2005). Performance of Geometrix ActiveID TM3D Face Recognition Engine on the FRGC Data. In:
  13. (2005). Recognizing facial expression: machine learning and application to spontaneous behavior. In:
  14. (2002). Recognizing imprecisely localized, partially occluded, andexpression variant faces from a single sample per class.
  15. (1981). Studies of cue saliency. Perceiving and Remembering Faces
  16. (2003). Toward an a®ect-sensitive multimodal humancomputer interaction.

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