Support Vector Regression of Sparse Dictionary-Based Features for View-Independent Action Unit Intensity Estimation

In this paper, a robust system for viewindependent action unit intensity estimation is presented. Based on the theory of sparse coding, region-specific dictionaries are trained to approximate the characteristic of the individual action units. The system incorporates landmark detection, face alignment and contrast normalization to handle a large variety of different scenes. Coupled with head pose estimation, an ensemble of large margin classifiers is used to detect the individual action units. The experimental validation shows that our system is robust against pose variations and able to outperform the challenge baseline by more than 35%.

[1]  Mohammad H. Mahoor,et al.  Facial action unit recognition with sparse representation , 2011, Face and Gesture 2011.

[2]  Maja Pantic,et al.  Fully Automatic Facial Action Unit Detection and Temporal Analysis , 2006, 2006 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'06).

[3]  P. Ekman,et al.  Pan-Cultural Elements in Facial Displays of Emotion , 1969, Science.

[4]  D. Donoho,et al.  Sparse MRI: The application of compressed sensing for rapid MR imaging , 2007, Magnetic resonance in medicine.

[5]  Shiguang Shan,et al.  AU-aware Deep Networks for facial expression recognition , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

[6]  Fernando De la Torre,et al.  Supervised Descent Method and Its Applications to Face Alignment , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[7]  Yoram Bresler,et al.  ADMiRA: Atomic Decomposition for Minimum Rank Approximation , 2009, IEEE Transactions on Information Theory.

[8]  Michel Valstar,et al.  Advances, Challenges, and Opportunities in Automatic Facial Expression Recognition , 2016 .

[9]  Josephine Sullivan,et al.  One millisecond face alignment with an ensemble of regression trees , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[11]  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.

[12]  Fernando De la Torre,et al.  A Functional Regression Approach to Facial Landmark Tracking , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Lionel Prevost,et al.  Facial Action Recognition Combining Heterogeneous Features via Multikernel Learning , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[15]  Shaun J. Canavan,et al.  BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database , 2014, Image Vis. Comput..

[16]  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.

[17]  Marian Stewart Bartlett,et al.  Multilayer Architectures for Facial Action Unit Recognition , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Lei Zhang,et al.  Support Vector Guided Dictionary Learning , 2014, ECCV.

[19]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[20]  Zheng Zhang,et al.  FERA 2017 - Addressing Head Pose in the Third Facial Expression Recognition and Analysis Challenge , 2017, 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017).

[21]  Takeo Kanade,et al.  Evaluation of Gabor-wavelet-based facial action unit recognition in image sequences of increasing complexity , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[22]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[23]  Honggang Zhang,et al.  Deep Region and Multi-label Learning for Facial Action Unit Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Simon Lucey,et al.  Deformable Model Fitting by Regularized Landmark Mean-Shift , 2010, International Journal of Computer Vision.

[25]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .