The first facial expression recognition and analysis challenge

By Michel François Valstar, Bihan Jiang, Marc Mehu, Maja Pantic, Klaus R. Scherer. Face and Gesture 2011, 2011

405 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ef259f72a44a0675eae7b55fc7afe0e86305fce5

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Multi-Agent Distributed Lifelong Learning for Collective Knowledge Acquisition (2017, methodology): “We used four benchmark MTL datasets in our experiments, including two classification and two regression datasets: 1) land mine detection in radar images [36], 2) facial expression identification from photographs of a subject’s face [30], 3) predicting London students’ scores using school-specific an”
Cross-dataset learning and person-specific normalisation for automatic Action Unit detection (2015, methodology): “We refer the reader to recent surveys and challenges [6], [22], [23], [25].”
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark (2011, neutral): “Recently, the Facial Expression Recognition and Analysis Challenge (FERA) 2011 [22] competition was organised for comparing the state of the art temporal facial expression analysis methods.”
Going deeper in facial expression recognition using deep neural networks (2015, methodology): “[48] is the FERA 2011 challenge baseline and [2] is the result of UC Riverside team (winner of the challenge).”
Automatic Analysis of Facial Actions: A Survey (2019, methodology): “An adapted version of this heuristic was used in [188], as no annotations of the temporal segments were available.”
AVEC 2011-The First International Audio/Visual Emotion Challenge (2011, methodology): “They were also used as the baseline features for the recently held challenge on facial expression recognition and analysis (FERA 2011, [17]).”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, background): “As a consequence the Facial Expression and Recognition (FERA) challenge added a special section for intensity estimation [232], [233].”
OpenFace 2.0: Facial Behavior Analysis Toolkit (2018, methodology): “Our models are trained on DISFA [48], SEMAINE [51], BP4D [80], UNBC-McMaster [44], Bosphorus [59] and FERA 2011 [66] datasets.”
OpenFace: An open source facial behavior analysis toolkit (2016, methodology): “In order to reduce the feature dimensionality we use a PCA model trained on a number of facial expression datasets: CK+ [38], DISFA [41], AVEC 2011 [52], FERA 2011 [60], and FERA 2015 [59].”
Learning Transferable Knowledge Through Embedding Spaces (2019, methodology): “Facial Expression Recognition: This dataset consists of binary facial expression recognition tasks [260].”
Exploiting sparsity and co-occurrence structure for action unit recognition (2015, methodology): “We followed the protocol used in the FERA challenge [28], using 87 sequences (5,172 frames; 7 subjects) that were FACS coded and available as training data.”
ELLA: An Efficient Lifelong Learning Algorithm (2013, background): “Facial Expression Recognition This data set is from a recent facial expression recognition challenge (Valstar et al., 2011).”
DISFA: A Spontaneous Facial Action Intensity Database (2013, background): “Automatic spontaneous facial expression analysis faces several challenges [40].”
AVEC 2012: the continuous audio/visual emotion challenge (2012, methodology): “They were also used as the baseline features for the recently held challenge on facial expression recognition and analysis (FERA 2011, [19]).”