Facial Micro-Expressions Grand Challenge 2018 Summary

Moi Hoon Yap · John See · Xiaopeng Hong · Sujing Wang

60 citations0/0 files storedInternational Conference on Automatic Face and Gesture Recognition

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Emotion and Mood Recognition Face recognition and analysis Face and Expression Recognition

Abstract

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.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2014CASME II: An Improved Spontaneous Micro-Expression Database and the Baseline Evaluation 2016SAMM: A Spontaneous Micro-Facial Movement Dataset 2015A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition 2010Cross-Corpus Acoustic Emotion Recognition: Variances and Strategies 2009Facial micro-expressions recognition using high speed camera and 3D-gradient descriptor 2010The MUG facial expression database 2017CAS(ME): A Database for Spontaneous Macro-Expression and Micro-Expression Spotting and Recognition

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2019Extended Local Binary Patterns for Efficient and Robust Spontaneous Facial Micro-Expression Recognition 2020A survey of micro-expression recognition 2019Spotting Micro-Expressions on Long Videos Sequences

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