Expression-Compensated 3D Face Recognition with Geodesically Aligned Bilinear Models

In this paper, we present a technique for addressing 3-dimensional face recognition in presence of facial expressions using a bilinear model. The bilinear model allows decoupling the impact of identity and expression on face appearance and encoding their contribution in separate control parameters. This is achieved by first representing faces as parametric surface models described by a fixed length parameter vector. A generic face model is fitted to each face based on a novel technique that relies on geodesic distances to find implicitly corresponding facial landmarks between the model and the face in hand. Model parameters are then used for bilinear decomposition. The experimental results on the publicly available BU-3DFE face database demonstrate the effectiveness of our technique.

[1]  Joshua B. Tenenbaum,et al.  Separating Style and Content with Bilinear Models , 2000, Neural Computation.

[2]  Patrick J. Flynn,et al.  Multiple Nose Region Matching for 3D Face Recognition under Varying Facial Expression , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Narendra Ahuja,et al.  Facial expression decomposition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Ioannis A. Kakadiaris,et al.  Three-Dimensional Face Recognition in the Presence of Facial Expressions: An Annotated Deformable Model Approach , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Alexander M. Bronstein,et al.  Expression-Invariant Representations of Faces , 2007, IEEE Transactions on Image Processing.

[6]  Christian R. Shelton,et al.  Morphable Surface Models , 2000, International Journal of Computer Vision.

[7]  Michael G. Strintzis,et al.  3-D Face Recognition With the Geodesic Polar Representation , 2007, IEEE Transactions on Information Forensics and Security.

[8]  Jun Wang,et al.  A 3D facial expression database for facial behavior research , 2006, 7th International Conference on Automatic Face and Gesture Recognition (FGR06).

[9]  Hong Qin,et al.  A novel FEM-based dynamic framework for subdivision surfaces , 2000, Comput. Aided Des..

[10]  Demetri Terzopoulos,et al.  Multilinear subspace analysis of image ensembles , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[11]  Zoran Popovic,et al.  The space of human body shapes: reconstruction and parameterization from range scans , 2003, ACM Trans. Graph..

[12]  Patrick J. Flynn,et al.  A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition , 2006, Comput. Vis. Image Underst..

[13]  Ioannis A. Kakadiaris,et al.  Elastically Adaptive Deformable Models , 1996, ECCV.

[14]  Hao Zhang,et al.  Adapting Geometric Attributes for Expression-Invariant 3D Face Recognition , 2007, IEEE International Conference on Shape Modeling and Applications 2007 (SMI '07).