A study of non-frontal-view facial expressions recognition

The existing methods of facial expression recognition are typically based on the near-frontal face data. The analysis of non-frontal-view facial expression is a largely unexplored research. The accessibility to a recent 3D facial expression database (BU-3DFE database) motivates us to explore an interesting question: whether non-frontal-view facial expression analysis can achieve the same as or better performance than the existing frontal-view facial expression method. Our extensive recognition experiments on data of 100 subjects with 5 yaw rotation view angles suggests that the non-frontal-view facial expression classification can outperform frontal-view facial expression recognition, given the manually labeled facial key points.

[1]  Takeo Kanade,et al.  Comprehensive database for facial expression analysis , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[2]  Nicu Sebe,et al.  Authentic facial expression analysis , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[3]  Yuxiao Hu,et al.  Learning a locality preserving subspace for visual recognition , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[4]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Gwen Littlewort,et al.  Recognizing facial expression: machine learning and application to spontaneous behavior , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[6]  Jeffrey F. Cohn,et al.  Foundations of human computing: facial expression and emotion , 2006, ICMI '06.

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

[8]  J. Russell,et al.  Facial and vocal expressions of emotion. , 2003, Annual review of psychology.

[9]  Qiang Ji,et al.  An automated face reader for fatigue detection , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[10]  Luiz Velho,et al.  Automatic 3D Facial Expression Analysis in Videos , 2005, AMFG.

[11]  Jun Wang,et al.  3D Facial Expression Recognition Based on Primitive Surface Feature Distribution , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[12]  Ashish Kapoor,et al.  Automatic prediction of frustration , 2007, Int. J. Hum. Comput. Stud..

[13]  Maja Pantic,et al.  Dynamics of facial expression: recognition of facial actions and their temporal segments from face profile image sequences , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[14]  PanticMaja,et al.  A Survey of Affect Recognition Methods , 2009 .