EmotioNet: An Accurate, Real-Time Algorithm for the Automatic Annotation of a Million Facial Expressions in the Wild

By Carlos Fabian Benitez-Quiroz, Ramprakash Srinivasan, Aleix M. Martinez. Computer Vision and Pattern Recognition, 2016

577 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/cbf4a4bbfadf0c8321da579075f14b997b1354ad

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Citations in the corpus

Facial Expression Analysis under Partial Occlusion (2018, methodology): “This study was later extensively extended in the 17th century to explore the relationship between facial expressions and the movements of head muscles (Bettadapura 2012). Since then, one pioneering work that has significantly impacted today’s automatic FEA systems was done by Charles Darwin, who pro”
Learning Residual Images for Face Attribute Manipulation (2016, background): “Considerable progresses have been made on face image processing, such as age analysis [22][26], emotion detection [1][5] and attribute classification [4][20][15][18].”
Context-Aware Emotion Recognition Networks (2019, methodology): “Previous researches for emotion recognition based on handcrafted features [4, 5] or deep networks [6, 7, 8] have mainly focused on the perception of the facial expression, based on the assumption that facial images are one of the most discriminative features of emotional responses.”
Deep Facial Expression Recognition: A Survey (2018, methodology): “A total of 950,000 images were annotated by the automatic action unit (AU) detection model in [43], and the remaining 25,000 images were manually annotated with 11”
Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition (2019, background): “With the popularity of data-driven deep learning techniques, several recent efforts on FER have been made on the collection of large-scale datasets [19], [44], [33], and many works [50], [27], [37], [5], [1], [32] exploit deep convolutional neural networks (CNN) to improve the performance of FER.”
AffectNet: A Database for Facial Expression, Valence, and Arousal Computing in the Wild (2017, methodology): “The EmotioNet [22] consists of one million images of facial expressions downloaded from the Internet by selecting all the words derived from the word “feeling” inWordNet [31].”
GANimation: Anatomically-aware Facial Animation from a Single Image (2018, methodology): “We have exhaustively evaluated the model capabilities and limits in the EmotioNet [3] and RaFD [16] datasets as well as in images from movies.”
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.”
IExpressNet: Facial Expression Recognition with Incremental Classes (2020, background): “To solve the problem of the domain gap, researchers extend the data distribution from lab-control to that in the wild [19, 33, 47].”
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.”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Unconstrained Facial Expression Recognition (2019, background): “EmotioNet [4] is a large-scale database with one million facial expression images collected from the Internet.”
Expression, Affect, Action Unit Recognition: Aff-Wild2, Multi-Task Learning and ArcFace (2019, background): “In [2] a static in-the-wild database (Emotionet) has been created that contains AU annotations for 1 M images.”
A Brief Review of Facial Emotion Recognition Based on Visual Information (2018, methodology): “A large-scale dataset and massive computing power are required for training as the structure becomes increasingly deep.”
Reliable Crowdsourcing and Deep Locality-Preserving Learning for Expression Recognition in the Wild (2017, methodology): “E-motioNet [1] is a large database of one million facial expression images in the wild created by an automatic AU detection algorithm.”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “Social signal processing mainly involves facial expression recognition [88,65,40,59,75,14,41,39,28,47,14,89].”