Classification of spontaneous and posed smiles

Automatic detection of affective states from facial images and videos is a very important application in social signal processing. In this study we focus on the expression of happiness, and propose a physiologically inspired approach for detecting whether a smile is spontaneously produced or not. We use distance-based and angular features that characterize movements of facial regions, and train local classifiers for smile classification. Our experiments with different classifiers on two different datasets show that the eyelid region can be very useful in assessing spontaneous versus posed smiles.

[1]  Eva Krumhuber,et al.  Moving Smiles: The Role of Dynamic Components for the Perception of the Genuineness of Smiles , 2005 .

[2]  Thomas S. Huang,et al.  Facial Expression Recognition from Video Sequences : Temporal and Static Modelling , 2002 .

[3]  Maja Pantic,et al.  Automatic Analysis of Facial Expressions: The State of the Art , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  H. Leder,et al.  When context hinders! Learn–test compatibility in face recognition , 2005, The Quarterly journal of experimental psychology. A, Human experimental psychology.

[5]  T. Sejnowski,et al.  Face image analysis for expression measurement and detection of deceit , 1999 .

[6]  P. Ekman,et al.  Felt, false, and miserable smiles , 1982 .

[7]  Hatice Gunes,et al.  How to distinguish posed from spontaneous smiles using geometric features , 2007, ICMI '07.

[8]  Jeffrey F. Cohn,et al.  The Timing of Facial Motion in posed and Spontaneous Smiles , 2003, Int. J. Wavelets Multiresolution Inf. Process..

[9]  Nicu Sebe,et al.  Facial expression recognition from video sequences: temporal and static modeling , 2003, Comput. Vis. Image Underst..

[10]  J. Cohn,et al.  Movement Differences between Deliberate and Spontaneous Facial Expressions: Zygomaticus Major Action in Smiling , 2006, Journal of nonverbal behavior.

[11]  Jing Xiao,et al.  Multimodal coordination of facial action, head rotation, and eye motion during spontaneous smiles , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[12]  Thomas S. Huang,et al.  Connected vibrations: a modal analysis approach for non-rigid motion tracking , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Miguel A. Ferrer,et al.  gpdsHMM : A HIDDEN MARKOV MODEL TOOLBOX IN THE MATLAB ENVIRONMENT , 2004 .

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

[15]  Qiang Ji,et al.  Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  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).