Expression robust 3D face recognition via mesh-based histograms of multiple order surface differential quantities
Type
Conference PaperKAUST Department
Visual Computing Center (VCC)Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2011-09Permanent link to this record
http://hdl.handle.net/10754/564435
Metadata
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This paper presents a mesh-based approach for 3D face recognition using a novel local shape descriptor and a SIFT-like matching process. Both maximum and minimum curvatures estimated in the 3D Gaussian scale space are employed to detect salient points. To comprehensively characterize 3D facial surfaces and their variations, we calculate weighted statistical distributions of multiple order surface differential quantities, including histogram of mesh gradient (HoG), histogram of shape index (HoS) and histogram of gradient of shape index (HoGS) within a local neighborhood of each salient point. The subsequent matching step then robustly associates corresponding points of two facial surfaces, leading to much more matched points between different scans of a same person than the ones of different persons. Experimental results on the Bosphorus dataset highlight the effectiveness of the proposed method and its robustness to facial expression variations. © 2011 IEEE.Citation
Li, H., Huang, D., Lemaire, P., Morvan, J.-M., & Chen, L. (2011). Expression robust 3D face recognition via mesh-based histograms of multiple order surface differential quantities. 2011 18th IEEE International Conference on Image Processing. doi:10.1109/icip.2011.6116308Conference/Event name
2011 18th IEEE International Conference on Image Processing, ICIP 2011ISBN
9781457713033ae974a485f413a2113503eed53cd6c53
10.1109/ICIP.2011.6116308