Meta-Analysis of the First Facial Expression Recognition Challenge

By Michel François Valstar, Marc Mehu, Bihan Jiang, Maja Pantic, Klaus R. Scherer. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2012

306 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/c5366f412f2e8e78280afcccc544156f63b516e3

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Citations in the corpus

A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “2001; Jaimes and Sebe 2007; Pang and Lee 2008; Pantic and Rothkrantz 2003]) and more recent surveys (2009 to present – see [Calvo and D’Mello 2010; D'Mello and Kory 2012; Valstar et al. 2012; Zeng, Pantic, Roisman and Huang 2009]), and will not be repeated here.”
Automatic Analysis of Facial Actions: A Survey (2019, background): “In fact, automatic message judgement in terms of basic emotions is considered a solved problem nowadays, while machine analysis of AUs is still an open challenge [184].”
Facial Expression Recognition via a Boosted Deep Belief Network (2014, methodology): “These features can be either hand-designed [32, 33, 34, 25, 1, 27, 28, 26, 22, 9, 4, 10, 13] or learned from training images [6, 29, 15, 16, 36, 2, 35, 30].”
A Systematic Review on Affective Computing: Emotion Models, Databases, and Recent Advances (2022, background): “The GEMEP database is one of the few databases that have frame-by-frame AU labels [94].”
Video and Image based Emotion Recognition Challenges in the Wild: EmotiW 2015 (2015, background): “The parameters are: range = [0-360], pyramid levels = 3 and number of bins = 16.”
Learning to Transfer: Transferring Latent Task Structures and Its Application to Person-Specific Facial Action Unit Detection (2015, background): “Of those, AU12 (a smile) is probably the most paradigmatic case, as smiles are easily identifiable (see for example the results on the FERA challenge [23]).”
Selective Transfer Machine for Personalized Facial Action Unit Detection (2013, methodology): “The GEMEP-FERA [32] defined PS in this way.”
From Facial Expression Recognition to Interpersonal Relation Prediction (2016, methodology): “A variety of hand-crafted features have been employed, such as LBP [69,88], HOG [8], and SIFT [22] features.”
Survey on Emotional Body Gesture Recognition (2018, unknown): “Selected samples from databases containing gesture based expressions of affect: (a) FABO [21], (b) GEMEP-FERA [93], [144], [145], (c) Theater [92], (d) HUMAINE [94], [104], [105], [146], (e) LIRIS-ACCEDE [147], [148], and (f) MSR-Action 3D [149].”
Deep Facial Expression Recognition: A Survey (2018, background): “Additionally, high inter-subject variations exist due to different personal attributes, such as age, gender, ethnic backgrounds and level of expressiveness [32].”
Emotional contagion in organizational life (2018, unknown): “The development of automated facial coding software offers the hope of further improving researchers’ ability to video-code emotions (Valstar et al., 2012), particularly in situations where emotional contagion needs to be coded in very small units of time,”
Deeply Learning Deformable Facial Action Parts Model for Dynamic Expression Analysis (2014, methodology): “For FERA, as the labels on test set are unreleased, we adopt leave-one-subject-out cross-validation on the training set.”