Facial Micro-Expressions Grand Challenge 2018 Summary

This paper summarises the Facial Micro-Expression Grand Challenge (MEGC 2018) held in conjunction with the 13th IEEE Conference on Automatic Face and Gesture Recognition (FG) 2018. In this workshop, we aim to stimulate new ideas and techniques for facial micro-expression analysis by proposing a new cross-database challenge. Two state-of-the-art datasets, CASME II and SAMM, are used to validate the performance of existing and new algorithms. Also, the challenge advocates the recognition of micro-expressions based on AU-centric objective classes rather than emotional classes. We present a summary and analysis of the baseline results using LBP-TOP, HOOF and 3DHOG, together with results from the challenge submissions.

[1]  Yuichi Ohta,et al.  Facial Micro-Expression Detection in Hi-Speed Video Based on Facial Action Coding System (FACS) , 2013, IEICE Trans. Inf. Syst..

[2]  John See,et al.  Spontaneous Subtle Expression Recognition: Imbalanced Databases and Solutions , 2014, ACCV.

[3]  Guoying Zhao,et al.  CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation , 2014, PloS one.

[4]  Matti Pietikäinen,et al.  Spotting Rapid Facial Movements from Videos Using Appearance-Based Feature Difference Analysis , 2014, 2014 22nd International Conference on Pattern Recognition.

[5]  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.

[6]  Moi Hoon Yap,et al.  Micro-Facial Movement Detection Using Individualised Baselines and Histogram-Based Descriptors , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[7]  Nicholas Costen,et al.  SAMM: A Spontaneous Micro-Facial Movement Dataset , 2018, IEEE Transactions on Affective Computing.

[8]  Yuichi Ohta,et al.  Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor , 2009, ICDP.

[9]  Nicholas Costen,et al.  Micro-Facial Movements: An Investigation on Spatio-Temporal Descriptors , 2014, ECCV Workshops.

[10]  John C. Platt,et al.  Fast training of support vector machines using sequential minimal optimization, advances in kernel methods , 1999 .

[11]  Anastasios Delopoulos,et al.  The MUG facial expression database , 2010, 11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10.

[12]  Guoying Zhao,et al.  A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition , 2016, IEEE Transactions on Affective Computing.

[13]  Andrew Zisserman,et al.  Deep Face Recognition , 2015, BMVC.

[14]  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.

[15]  Michael J. Lyons,et al.  Coding facial expressions with Gabor wavelets , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[16]  Joachim Denzler,et al.  ImageNet pre-trained models with batch normalization , 2016, ArXiv.

[17]  Trevor Darrell,et al.  Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[18]  Dmitry B. Goldgof,et al.  Towards macro- and micro-expression spotting in video using strain patterns , 2009, 2009 Workshop on Applications of Computer Vision (WACV).

[19]  KokSheik Wong,et al.  Spontaneous Subtle Expression Detection and Recognition based on Facial Strain , 2016, Signal Process. Image Commun..

[20]  Moi Hoon Yap,et al.  Objective Classes for Micro-Facial Expression Recognition , 2017, J. Imaging.

[21]  Matti Pietikäinen,et al.  Facial expression recognition from near-infrared videos , 2011, Image Vis. Comput..

[22]  Xiaolan Fu,et al.  CAS(ME)$^2$ : A Database for Spontaneous Macro-Expression and Micro-Expression Spotting and Recognition , 2018, IEEE Transactions on Affective Computing.