Emotion Recognition In The Wild Challenge 2014

Abhinav Dhall · Roland Goecke · Jyoti Joshi · Karan Sikka · Tom Gedeon

216 citations0/0 files storedINTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION

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

Abstract

The Second Emotion Recognition In The Wild Challenge (EmotiW) 2014 consists of an audio-video based emotion classification challenge, which mimics the real-world conditions. Traditionally, emotion recognition has been performed on data captured in constrained lab-controlled like environment. While this data was a good starting point, such lab controlled data poorly represents the environment and conditions faced in real-world situations. With the exponential increase in the number of video clips being uploaded online, it is worthwhile to explore the performance of emotion recognition methods that work `in the wild'. The goal of this Grand Challenge is to carry forward the common platform defined during EmotiW 2013, for evaluation of emotion recognition methods in real-world conditions. The database in the 2014 challenge is the Acted Facial Expression In Wild (AFEW) 4.0, which has been collected from movies showing close-to-real-world conditions. The paper describes the data partitions, the baseline method and the experimental protocol.

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References

2002Comprehensive 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 2006Automatic Recognition of Facial Actions in Spontaneous Expressions 2009OpenEAR — Introducing the munich open-source emotion and affect recognition toolkit 2013Combining modality specific deep neural networks for emotion recognition in video 2009Toward Practical Smile Detection 2011AVEC 2011–The First International Audio/Visual Emotion Challenge

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2017Facial Expression Recognition Using Weighted Mixture Deep Neural Network Based on Double-Channel Facial Images 2014Combining Multiple Kernel Methods on Riemannian Manifold for Emotion Recognition in the Wild 2018Covariance Pooling for Facial Expression Recognition 2023Py-Feat: Python Facial Expression Analysis Toolbox 2023Understanding Deep Learning Techniques for Recognition of Human Emotions Using Facial Expressions: A Comprehensive Survey

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