Facial Expression Analysis

By Fernando De la Torre, Jeffrey F. Cohn. 2011

689 citations recorded by the source corpus.

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

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Multimodal Transformer for Unaligned Multimodal Language Sequences (2019, methodology): “We use Facet (iMotions, 2017) to indicate 35 facial action units, which records facial muscle movement (Ekman et al., 1980; Ekman, 1992) for representing per-frame basic and advanced emotions.”
Robust Facial Expression Recognition Based on Local Directional Pattern (2010, background): “Two types of approaches have been proposed to extract facial features for expression recognition: a geometric feature-based system and an appearance-based system [12].”
OpenFace 2.0: Facial Behavior Analysis Toolkit (2018, methodology): “As the face is a very important channel of nonverbal communication [23], [20], facial behavior analysis has been used in different applications to facilitate human computer interaction [47], [50].”
Learning Factorized Multimodal Representations (2018, methodology): “Visual: We use Facet (iMotions, 2017) to extract a set of features including per-frame basic and advanced emotions and facial action units as indicators of facial muscle movement (Ekman et al., 1980; Ekman, 1992).”
Painful data: The UNBC-McMaster shoulder pain expression archive database (2011, background): “Automated facial expression detection has made great strides over the past decade [1], [2], [3].”
Integrating Multimodal Information in Large Pretrained Transformers (2020, methodology): “Visual: For the visual modality, the Facet library (iMotions, 2017) is used to extract a set of visual features including facial action units, facial landmarks, head pose, gaze tracking and HOG features.”
Social signal processing: Survey of an emerging domain (2009, background): “Most of the proposed approaches to facial expression recognition are directed toward static, analytic, 2-D facial feature extraction [135][185].”
Multi-attention Recurrent Network for Human Communication Comprehension (2018, methodology): “Vision Facet (iMotions 2017) is used to extract a set of features including per-frame basic and advanced emotions and facial action units as indicators of facial muscle movement.”
Multimodal Machine Learning: A Survey and Taxonomy (2017, neutral): “landmark detection, and facial expression recognition [46].”
Meta-Analysis of the First Facial Expression Recognition Challenge (2012, background): “For an overview of more recent work by researchers at CMU, see [29].”
Factorized Multimodal Transformer for Multimodal Sequential Learning (2019, methodology): “Visual: For the visual modality, the Emotient FACET (iMotions, 2017) is used to extract a set of visual features including Facial Action Units (Ekman et al., 1980), visual indicators of emotions, and sparse facial landmarks.”
Selective Transfer Machine for Personalized Facial Action Unit Detection (2013, background): “Interested readers may refer to [11, 21, 32] for more complete surveys of AU detection methods.”
The Extended Cohn-Kanade Dataset (CK+): A complete dataset for action unit and emotion-specified expression (2010, background): “Signiﬁcant advances have been made in the ﬁeld over the past decade [21, 22, 27] with increasing interest in non-posed facial behavior in naturalistic contexts [4, 17, 25] and posed data recorded from multiple views [12, 19] and 3D imaging [26].”