View-Invariant Method for Calculating 2D Optical Strain

Two-dimensional optical strain maps have been shown to be a useful feature that describes a bio-mechanical property of facial skin tissue during the non-rigid motion that occurs during facial expressions. In this paper, we propose a method for accurately estimating and modeling the three-dimensional strain impacted onto the face and demonstrate its robustness at different depth resolutions and views. Experimental results are given for a publically available dataset that contains high depth resolutions of facial expressions, as well as a new dataset collected using the Microsoft Kinect synchronized with two HD webcams.

[1]  Hao Li,et al.  Realtime performance-based facial animation , 2011, ACM Trans. Graph..

[2]  Yiannis Aloimonos,et al.  Spatio-Temporal Stereo Using Multi-Resolution Subdivision Surfaces , 2004, International Journal of Computer Vision.

[3]  Lijun Yin,et al.  A high-resolution 3D dynamic facial expression database , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[4]  Wojciech Matusik,et al.  Multi-scale capture of facial geometry and motion , 2007, ACM Trans. Graph..

[5]  Taku Komura,et al.  Spatial relationship preserving character motion adaptation , 2010, SIGGRAPH 2010.

[6]  Dmitry B. Goldgof,et al.  Macro- and micro-expression spotting in long videos using spatio-temporal strain , 2011, Face and Gesture 2011.

[7]  Jean Ponce,et al.  Dense 3D motion capture from synchronized video streams , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[8]  Dmitry B. Goldgof,et al.  Face recognition under camouflage and adverse illumination , 2010, 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS).

[9]  Derek Bradley,et al.  High resolution passive facial performance capture , 2010, SIGGRAPH 2010.

[10]  Ming Ouhyoung,et al.  Mirror MoCap: Automatic and efficient capture of dense 3D facial motion parameters from video , 2005, The Visual Computer.

[11]  Michael A. Penna.  The incremental approximation of nonrigid motion , 1994 .

[12]  Richard Bowden,et al.  Kinecting the dots: Particle based scene flow from depth sensors , 2011, 2011 International Conference on Computer Vision.

[13]  Jean Ponce,et al.  Dense 3D motion capture for human faces , 2009, CVPR.

[14]  Olivier D. Faugeras,et al.  Multi-View Stereo Reconstruction and Scene Flow Estimation with a Global Image-Based Matching Score , 2007, International Journal of Computer Vision.

[15]  Dmitry B. Goldgof,et al.  Evaluation of Facial Reconstructive Surgery on Patients with Facial Palsy Using Optical Strain , 2011, CAIP.

[16]  Tomaso A. Poggio,et al.  Reanimating Faces in Images and Video , 2003, Comput. Graph. Forum.

[17]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.