Dynamics of Facial Expression Extracted Automatically from Video

By Gwen Littlewort, M. Bartlett, Ian R. Fasel, J. Susskind, Javier R. Movellan. 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004

501 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/dfb7ccd06dca95161305331b61d6837ee72a6d9f

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Citations in the corpus

Survey on RGB, 3D, Thermal, and Multimodal Approaches for Facial Expression Recognition: History, Trends, and Affect-related Applications (2016, methodology): “While a large number of approaches aimed at detecting primary FEs or a limited set of FACS AUs [99], [116], [134], [137], others focused on a”
Robust facial expression recognition using local binary patterns (2005, methodology): “Linear Polynomial RBF LBP+SVM 87.2% 88.4% 87.6% Gabor+SVM [5] 84.8% worse than linear/RBF 86.9% Table 2.”
Coarse scales are sufficient for efficient categorization of emotional facial expressions: Evidence from neural computation (2010, methodology): “The purpose of the present article is to determine whether LSF components are sufficient for efficient categorization of an EFE.”
Recognizing facial expression: machine learning and application to spontaneous behavior (2005, unknown): “The images were converted into a Gabor magnitude representation, using a bank of Gabor filters at 8 orientations and 9 spatial frequencies (2:32 pixels per cycle at 1/2 octave steps) (See [9] and [10]).”
A 3D facial expression database for facial behavior research (2006, methodology): “A number of techniques were successfully developed using 2D static images or video sequences, including machine vision techniques [44, 12, 10, 21, 4, 41, 42] and machine learning techniques [1, 20, 5, 6, 45].”
Learning active facial patches for expression analysis (2012, methodology): “However, to fairly compare with previous work [12, 16], SVM is adopted to learn the expression classifiers and the oneagainst-all strategy is employed to decompose the six class problem into multiple binary classes problem.”
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions (2014, background): “In contrast to the more thoroughly studied domains of automatic basic emotion recognition (happy, sad, angry, disgusted, fearful, surprised, or neutral) [33], [6], [61] or facial action unit classification [37], [27], [7], [47] (from the Facial Action Coding System [17]), affective states that are r”
Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “In order to generalize across image collection environments, we have to collect large training datasets with variations in image conditions [59].”
On deep generative models with applications to recognition (2011, methodology): “The latter method and its variants [14] are considered a strong baseline in the literature.”
Deep Region and Multi-label Learning for Facial Action Unit Detection (2016, methodology): “To tackle AU detection under complex conditions, a majority of studies have been devoted to various features [1, 6, 13, 15, 19] and classifiers [2, 11, 30, 34, 36, 38].”
Coarse scales are sufﬁcient for efﬁcient categorization of emotional facial expressions: Evidence from neural computation (unknown): “Finally, [15,4] have shown that the convolution of Gabor wavelets, which are sensitive to different wavelengths and different orientations using a sliding window applied to the entire image permits reliable categorization, which is comparable to human data when the wavelets are associated with an…”
Social signal processing: Survey of an emerging domain (2009, background): “, [103][108]) and towards automatic discrimination between spontaneous and posed facial behaviour such as smiles [195], frowns [197], and pain [104], have been recently reported as well.”