Improved facial expression recognition via uni-hyperplane classification

Large margin learning approaches, such as support vector machines (SVM), have been successfully applied to numerous classification tasks, especially for automatic facial expression recognition. The risk of such approaches however, is their sensitivity to large margin losses due to the influence from noisy training examples and outliers which is a common problem in the area of affective computing (i.e., manual coding at the frame level is tedious so coarse labels are normally assigned). In this paper, we leverage the relaxation of the parallel-hyperplanes constraint and propose the use of modified correlation filters (MCF). The MCF is similar in spirit to SVMs and correlation filters, but with the key difference of optimizing only a single hyperplane. We demonstrate the superiority of MCF over current techniques on a battery of experiments.

[1]  D. Casasent,et al.  Minimum average correlation energy filters. , 1987, Applied optics.

[2]  Dimitri P. Bertsekas,et al.  Nonlinear Programming , 1997 .

[3]  G. Wahba.  Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV 1 , 1998 .

[4]  Yuh-Jye Lee,et al.  SSVM: A Smooth Support Vector Machine for Classification , 2001, Comput. Optim. Appl..

[5]  Timothy F. Cootes,et al.  Active Appearance Models , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Glenn Fung,et al.  Proximal support vector machine classifiers , 2001, KDD '01.

[8]  P. Khosla,et al.  Face Verification using Correlation Filters , 2002 .

[9]  T. Poggio,et al.  Regularized Least-Squares Classification 133 In practice , although , 2007 .

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

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

[12]  Jing Peng,et al.  SVM vs regularized least squares classification , 2004, ICPR 2004.

[13]  Dinggang Shen,et al.  Increasing Efficiency of SVM by Adaptively Penalizing Outliers , 2005, EMMCVPR.

[14]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Koby Crammer,et al.  Robust Support Vector Machine Training via Convex Outlier Ablation , 2006, AAAI.

[16]  Olvi L. Mangasarian,et al.  Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Gwen Littlewort,et al.  Automatic Recognition of Facial Actions in Spontaneous Expressions , 2006, J. Multim..

[18]  Reshma Khemchandani,et al.  Twin Support Vector Machines for Pattern Classification , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[19]  Jie Li,et al.  Training robust support vector machine with smooth Ramp loss in the primal space , 2008, Neurocomputing.

[20]  Salina Abdul Samad,et al.  A UMACE Filter Approach to Lipreading in Biometric Authentication System , 2008 .

[21]  Simon Lucey,et al.  Face alignment through subspace constrained mean-shifts , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  Simon Lucey,et al.  Registration Invariant Representations for Expression Detection , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[23]  Takeo Kanade,et al.  The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[24]  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.

[25]  Xi-chuan Zhou,et al.  Integrating outlier filtering in large margin training , 2011, Journal of Zhejiang University SCIENCE C.

[26]  Sridha Sridharan,et al.  Person-independent facial expression detection using Constrained Local Models , 2011, Face and Gesture 2011.

[27]  Maja Pantic,et al.  Action unit detection using sparse appearance descriptors in space-time video volumes , 2011, Face and Gesture 2011.

[28]  Tamás D. Gedeon,et al.  Emotion recognition using PHOG and LPQ features , 2011, Face and Gesture 2011.

[29]  Jeffrey F. Cohn,et al.  Painful data: The UNBC-McMaster shoulder pain expression archive database , 2011, Face and Gesture 2011.

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

[31]  Xuegong Zhang,et al.  An Improved Support Vector Machine Using Class-Median Vectors * , .

[32]  K. Schittkowski,et al.  NONLINEAR PROGRAMMING , 2022 .