Emotion recognition from an ensemble of features

This work details the authors' efforts to push the baseline of expression recognition performance on a realistic database. Both subject-dependent and subject-independent emotion recognition scenarios are addressed in this work. These two happen frequently in real life settings. The approach towards solving this problem involves face detection, followed by key point identification, then feature generation and then finally classification. An ensemble of features comprising of Hierarchial Gaussianization (HG), Scale Invariant Feature Transform (SIFT) and Optic Flow have been incorporated. In the classification stage we used SVMs. The classification task has been divided into person specific and person independent emotion recognition. Both manual labels and automatic algorithms for person verification have been attempted. They both give similar performance.

[1]  Jerry D. Gibson,et al.  Handbook of Image and Video Processing , 2000 .

[2]  Rogério Schmidt Feris,et al.  Manifold Based Analysis of Facial Expression , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[3]  Takeo Kanade,et al.  Recognizing Action Units for Facial Expression Analysis , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  K. Scherer,et al.  Introducing the Geneva Multimodal Emotion Portrayal (GEMEP) corpus , 2010 .

[5]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[6]  Maja Pantic,et al.  Motion history for facial action detection in video , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

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

[8]  Shuicheng Yan,et al.  SIFT-Bag kernel for video event analysis , 2008, ACM Multimedia.

[9]  Peter W. McOwan,et al.  A real-time automated system for the recognition of human facial expressions , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

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

[12]  Zhen Li,et al.  Hierarchical Gaussianization for image classification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[13]  Thomas S. Huang,et al.  3D facial expression recognition based on properties of line segments connecting facial feature points , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[14]  Horst Bischof,et al.  A Duality Based Approach for Realtime TV-L1 Optical Flow , 2007, DAGM-Symposium.

[15]  M. Bartlett,et al.  Machine Analysis of Facial Expressions , 2007 .

[16]  Gwen Littlewort,et al.  A Prototype for Automatic Recognition of Spontaneous Facial Actions , 2002, NIPS.

[17]  Thomas S. Huang,et al.  3D facial expression recognition based on automatically selected features , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[18]  Maja Pantic,et al.  The first facial expression recognition and analysis challenge , 2011, Face and Gesture 2011.

[19]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[20]  Henry Schneiderman,et al.  Learning a restricted Bayesian network for object detection , 2004, CVPR 2004.

[21]  Henry Schneiderman,et al.  Feature-centric evaluation for efficient cascaded object detection , 2004, CVPR 2004.

[22]  Alan C. Bovik,et al.  Handbook of Image and Video Processing (Communications, Networking and Multimedia) , 2005 .