Hough Forest-Based Facial Expression Recognition from Video Sequences

Automatic recognition of facial expression is a necessary step toward the design of more natural human-computer interaction systems. This work presents a user-independent approach for the recognition of facial expressions from image sequences. The faces are normalized in scale and rotation based on the eye centers' locations into tracks from which we extract features representing shape and motion. Classification and localization of the center of the expression in the video sequences are performed using a Hough transform voting method based on randomized forests. We tested our approach on two publicly available databases and achieved encouraging results comparable to the state of the art.

[1]  Shaogang Gong,et al.  Facial expression recognition based on Local Binary Patterns: A comprehensive study , 2009, Image Vis. Comput..

[2]  Luc Van Gool,et al.  A Hough transform-based voting framework for action recognition , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[3]  C. Darwin.  The Expression of the Emotions in Man and Animals , .

[4]  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).

[5]  Fadi Dornaika,et al.  Simultaneous Facial Action Tracking and Expression Recognition in the Presence of Head Motion , 2008, International Journal of Computer Vision.

[6]  Horst Bischof,et al.  Real-Time Tracking via On-line Boosting , 2006, BMVC.

[7]  Alex Pentland,et al.  Coding, Analysis, Interpretation, and Recognition of Facial Expressions , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Luc Van Gool,et al.  Action snippets: How many frames does human action recognition require? , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Kunihiko Fukushima,et al.  Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position , 1980, Biological Cybernetics.

[10]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[11]  Jitendra Malik,et al.  Multi-scale object detection by clustering lines , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[12]  Aggelos K. Katsaggelos,et al.  Automatic facial expression recognition using facial animation parameters and multistream HMMs , 2006, IEEE Transactions on Information Forensics and Security.

[13]  Luc Van Gool,et al.  Hough Transform-based Mouth Localization for Audio-visual Speech Recognition , 2009, BMVC.

[14]  Maja Pantic,et al.  Web-based database for facial expression analysis , 2005, 2005 IEEE International Conference on Multimedia and Expo.

[15]  Juergen Gall,et al.  Class-specific Hough forests for object detection , 2009, CVPR.

[16]  Mubarak Shah,et al.  Incremental action recognition using feature-tree , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[17]  Lifeng Shang,et al.  Nonparametric discriminant HMM and application to facial expression recognition , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Dana H. Ballard,et al.  Generalizing the Hough transform to detect arbitrary shapes , 1981, Pattern Recognit..

[19]  Jitendra Malik,et al.  Object detection using a max-margin Hough transform , 2009, CVPR.

[20]  Matti Pietikäinen,et al.  Boosted multi-resolution spatiotemporal descriptors for facial expression recognition , 2009, Pattern Recognit. Lett..

[21]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[22]  D J Field,et al.  Relations between the statistics of natural images and the response properties of cortical cells. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[23]  P. Ekman,et al.  Facial action coding system: a technique for the measurement of facial movement , 1978 .

[24]  Mohammed Yeasin,et al.  Recognition of facial expressions and measurement of levels of interest from video , 2006, IEEE Transactions on Multimedia.

[25]  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).

[26]  Nicu Sebe,et al.  Towards authentic emotion recognition , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[27]  P. Ekman,et al.  Constants across cultures in the face and emotion. , 1971, Journal of personality and social psychology.

[28]  Enrique Muñoz,et al.  Recognising facial expressions in video sequences , 2007, Pattern Analysis and Applications.

[29]  Marian Stewart Bartlett,et al.  Facial expression recognition using Gabor motion energy filters , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[30]  Gwen Littlewort,et al.  Dynamics of Facial Expression Extracted Automatically from Video , 2004, CVPR Workshops.

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

[32]  Larry S. Davis,et al.  Recognizing actions by shape-motion prototype trees , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[33]  Zhihong Zeng,et al.  A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions , 2009, IEEE Trans. Pattern Anal. Mach. Intell..

[34]  Theo Gevers,et al.  Accurate eye center location and tracking using isophote curvature , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.