Fully Automatic Recognition of the Temporal Phases of Facial Actions

By Michel François Valstar, Maja Pantic. IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics), 2012

340 citations recorded by the source corpus.

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Automatic Analysis of Facial Affect: A Survey of Registration, Representation, and Recognition (2015, methodology): “Valstar and Pantic [154] use a Gabor-based point localiser [162] and track the points using particle filter [107].”
Modeling transition patterns between events for temporal human action segmentation and classification (2015, methodology): “We describe the segment-level feature mapping in detail in Section IV.”
Selective Transfer Machine for Personalized Facial Action Unit Detection (2013, background): “The other is temporal modeling in which frames are segmented into sequences and modeled with a variant of Dynamic Bayesian Networks ( e.g. , Hidden Markov Models, Conditional Random Fields) [7, 26, 33].”
Static and dynamic 3D facial expression recognition: A comprehensive survey (2012, methodology): “In order to design cross-database experiments, a standard procedure has to be followed in 2D facial expression and AU analysis [124,125], and in order to test the real generalisation capabilities of 4D facial expression and AU recognition/analysis algorithms, more databases of posed 4D facial expres”
Automatic Analysis of Facial Actions: A Survey (2019, methodology): “[191], and for AU temporal segment detection using the probability yielded by a GentleBoost algorithm [88], [94].”