Emotion recognition in the wild challenge 2013

Abhinav Dhall · Roland Goecke · Jyoti Joshi · Michael Wagner · Tom Gedeon

179 citations0/0 files storedACM International Conference on Multimodal Interaction (ICMI)

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Emotion and Mood Recognition Sentiment Analysis and Opinion Mining Face and Expression Recognition

Abstract

Emotion recognition is a very active field of research. The Emotion Recognition In The Wild Challenge and Workshop (EmotiW) 2013 Grand Challenge consists of an audio-video based emotion classification challenges, which mimics real-world conditions. Traditionally, emotion recognition has been performed on laboratory controlled data. While undoubtedly worthwhile at the time, such laboratory controlled data poorly represents the environment and conditions faced in real-world situations. The goal of this Grand Challenge is to define a common platform for evaluation of emotion recognition methods in real-world conditions. The database in the 2013 challenge is the Acted Facial Expression in the Wild (AFEW), which has been collected from movies showing close-to-real-world conditions.

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References

2010The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression 2005Web-Based Database for Facial Expression Analysis 2012Collecting Large, Richly Annotated Facial-Expression Databases from Movies 2011Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark 2009OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit 2011The first facial expression recognition and analysis challenge 2009Toward Practical Smile Detection 2010The SEMAINE corpus of emotionally coloured character interactions

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2015Automatic Detection of Learning-Centered Affective States in the Wild 2018A Compact Deep Learning Model for Robust Facial Expression Recognition 2017Learning supervised scoring ensemble for emotion recognition in the wild 2021FER-net: facial expression recognition using deep neural net 2016EmotiW 2016: video and group-level emotion recognition challenges

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