Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

By Abhinav Dhall, Roland Göcke, Simon Lucey, Tom Gedeon. IEEE International Conference on Computer Vision, 2011

524 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/34a3d9d30e00550ab6a4f5ae5a8ecc0d1c3132c1

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Facial expression recognition with Convolutional Neural Networks: Coping with few data and the training sample order (2017, methodology): “Experiments were performed in the CK+ [4], MMI [54] and SFEW [55] datasets.”
Suppressing Uncertainties for Large-Scale Facial Expression Recognition (2020, background): “…have made signiﬁcant progress on facial expression recognition (FER) with algorithms and large-scale datasets, where datasets can be collected in lab-∗ oratory or in the wild, such as CK+ [29], MMI [39], Oulu-CASIA [47], SFEW/AFEW [10], FERPlus [4], AffectNet [32], EmotioNet [11], RAF-DB [22], etc.”
Image based Static Facial Expression Recognition with Multiple Deep Network Learning (2015, background): “Categories and Subject Descriptors I.5.4 [Pattern Recognition]: Applications—computer vision, signal processing ; I.4.m [Image Processing and Computer Vision]: Miscellaneous Keywords Facial Expression Recognition; Convolutional Neural Network; Multiple Network Learning; EmotiW 2015 Challenge”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “0 [83] 2015 1694 images Six basic expressions”
Deep Facial Expression Recognition: A Survey (2018, background): “The most commonly used version, SFEW 2.0, has been divided into three sets: Train, Val and Test.”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, background): “A static subset (SFEW [27]) is created by selecting some frames of AFEW.”
Going deeper in facial expression recognition using deep neural networks (2015, methodology): “We evaluate the proposed method on well-known publicly available facial expression databases: CMU MultiPIE [15], MMI [36], Denver Intensity of Spontaneous Facial Actions (DISFA) [29], extended CK+ [27], GEMEPFERA database [5], SFEW [10], and FER2013 [1].”
Context-Aware Emotion Recognition Networks (2019, background): “Recently, datasets recorded in the wild condition for including naturalistic emotion states [9, 25, 26] have attracted much attention.”
Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns (2015, background): “More recently, unconstrained, in-the-wild photos have been considered with the release of the EmotiW challenge [9].”
A Hybrid Deep Learning Architecture for Privacy-Preserving Mobile Analytics (2017, background): “Static Facial Expression in the Wild (SFEW) is an emotion detection benchmark [11].”
Collecting Large, Richly Annotated Facial-Expression Databases from Movies (2012, methodology): “Inspired by the Labeled Faces in the Wild (LFW) database,(1) we call our temporal database Acted Facial Expressions in the Wild (AFEW) and its static subset Static Facial Expressions in the Wild (SFEW).(2) In this context, ‘‘in the wild’’ refers to the challenging conditions in which the facial expr”
Data Augmentation in Emotion Classification Using Generative Adversarial Networks (2017, methodology): “In our experiments, three benchmark datasets are tested: Facial Expression Recognition Database (FER2013) [10], Static Facial Expressions in the Wild (SFEW) [5] and Japanese Female Facial Expression (JAFFE) Database [18].”
Facial Expression Recognition with Inconsistently Annotated Datasets (2018, background): “Datasets have sprung up for both in-the-lab and in-the-wild facial expressions, such as CK+[20], MMI[28], Oulu-CASIA[33], SFEW/AFEW[7], AffectNet[22], EmotioNet[2], RAF-DB[16], and others.”
Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “While previous RGB datasets record FEs in controlled lab environments, Acted Facial Expressions In The Wild Database (AFEW) [203], Affectiva-MIT Facial Expression Dataset (AMFED) [204] and SEMAINE [205] contain faces in naturalistic environments.”
Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning (2015, methodology): “Images are selected from movies, in a semi-automated way, via a system based on subtitles [5, 6].”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017, methodology): “0 [7] contains 700 images extracted from movies, and images were labelled by two independent labelers.”
Distract Your Attention: Multi-Head Cross Attention Network for Facial Expression Recognition (2021, background): “0 [44] is the newest version for SFEW dataset in which each facial emotion is extracted from the static frames of the AFEW video database.”