Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015

By Abhinav Dhall, O. V. Ramana Murthy, Roland Göcke, Jyoti Joshi, Tom Gedeon. International Conference on Multimodal Interaction, 2015

336 citations recorded by the source corpus.

The third Emotion Recognition in the Wild (EmotiW) challenge 2015 consists of an audio-video based emotion and static image based facial expression classification sub-challenges, which mimics real-world conditions. The two sub-challenges are based on the Acted Facial Expression in the Wild (AFEW) 5.0 and the Static Facial Expression in the Wild (SFEW) 2.0 databases, respectively. The paper describes the data, baseline method, challenge protocol and the challenge results. A total of 12 and 17 teams participated in the video based emotion and image based expression sub-challenges, respectively.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/ad0863aa16301d7c6617f8b965a64cf58f38594f

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Citations in the corpus

Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning (2015, methodology): “We also note that this improvement in accuracy from the validation to the test data is also observed in the organizer’s baseline paper [7], where their approach improved from an accuracy of 35.96% on the validation data to 39.13% on the test data.”
Recurrent Neural Networks for Emotion Recognition in Video (2015, background): “The Emotion recognition in the wild (EmotiW 2015) challenge [9] is an extension of a similar challenge held in 2014 [8].”
Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017, background): “The only challenge that uses ‘in-the-wild’ data is the series of [15, 14, 16].”
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (2018, background): “AFEW (Dhall et al., 2012, 2015) is a dynamic temporal facial expressions data corpus consisting of close to real world environment extracted from movies.”
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (2015, methodology): “Additional fine-tuning of the resulting networks the training set for the EmotiW 2015 data set [10] required one more day.”
Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition (2018, background): “It may fail in some challenging datasets like AFEW [66], where face images suffer from substantial viewpoint and illumination changes.”