Dynamic facial expression recognition using local patch and LBP-TOP

Local binary pattern on three orthogonal planes (LBP-TOP) is one of the most popular method for dynamic texture analysis and has been successfully applied to facial expression analysis. Yet an effective LBP-TOP operator highly relies on preprocessing. And, like many appearance-based approaches, this approach reserves more identity-related cues rather than expression. In this work, we propose a fully automatic approach for facial expression recognition based on points registration, localized patch extraction and LBP-TOP feature representation. The efficiency of this method is evaluated on CK+ database. Results show that the proposed method has achieved a better performance compared with existing methods.

[1]  Maja Pantic,et al.  Coupled Gaussian processes for pose-invariant facial expression recognition , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Maja Pantic,et al.  A Dynamic Appearance Descriptor Approach to Facial Actions Temporal Modeling , 2014, IEEE Transactions on Cybernetics.

[3]  Fernando De la Torre,et al.  Continuous AU intensity estimation using localized, sparse facial feature space , 2013, 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG).

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

[5]  Andrea Cavallaro,et al.  Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Qiang Ji,et al.  A Unified Probabilistic Framework for Spontaneous Facial Action Modeling and Understanding , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[8]  Di Huang,et al.  Local Binary Patterns and Its Application to Facial Image Analysis: A Survey , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[9]  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.

[10]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  Ville Ojansivu,et al.  Blur Insensitive Texture Classification Using Local Phase Quantization , 2008, ICISP.

[12]  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.

[13]  Rama Chellappa,et al.  Structure-Preserving Sparse Decomposition for Facial Expression Analysis , 2014, IEEE Transactions on Image Processing.

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

[15]  Maja Pantic,et al.  A Dynamic Texture-Based Approach to Recognition of Facial Actions and Their Temporal Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Maja Pantic,et al.  Fully Automatic Recognition of the Temporal Phases of Facial Actions , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[17]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  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).

[19]  Qiang Ji,et al.  Capturing Complex Spatio-temporal Relations among Facial Muscles for Facial Expression Recognition , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

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