Learning deformation model for expression-robust 3D face recognition

Zhe Guo; Shu Liu; Yi Wang; Tao Lei

doi:10.1117/12.2228002

9 December 2015 Learning deformation model for expression-robust 3D face recognition

Zhe Guo, Shu Liu, Yi Wang, Tao Lei

Proceedings Volume 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015); 98170O (2015) https://doi.org/10.1117/12.2228002
Event: Seventh International Conference on Graphic and Image Processing, 2015, Singapore, Singapore

Abstract

Expression change is the major cause of local plastic deformation of the facial surface. The intra-class differences with large expression change somehow are larger than the inter-class differences as it's difficult to distinguish the same individual with facial expression change. In this paper, an expression-robust 3D face recognition method is proposed by learning expression deformation model. The expression of the individuals on the training set is modeled by principal component analysis, the main components are retained to construct the facial deformation model. For the test 3D face, the shape difference between the test and the neutral face in training set is used for reconstructing the expression change by the constructed deformation model. The reconstruction residual error is used for face recognition. The average recognition rate on GavabDB and self-built database reaches 85.1% and 83%, respectively, which shows strong robustness for expression changes.

Citation Download Citation

Zhe Guo, Shu Liu, Yi Wang, and Tao Lei "Learning deformation model for expression-robust 3D face recognition", Proc. SPIE 9817, Seventh International Conference on Graphic and Image Processing (ICGIP 2015), 98170O (9 December 2015); https://doi.org/10.1117/12.2228002

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