Observer-based measurement of facial expression with the Facial Action Coding System.

By Jeffrey F. Cohn, Zara Ambadar, Paul Ekman. 2007

408 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/eafda8a94e410f1ad53b3e193ec124e80d57d095

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Citations in the corpus

Detecting depression from facial actions and vocal prosody (2009, background): “FACS annotation is arguably the current standard for measuring facial actions [ 26 ].”
BP4D-Spontaneous: a high-resolution spontaneous 3D dynamic facial expression database (2014, methodology): “In Section 3, we describe the creation of meta-data, including FACS coding, 3D/2D feature tracking, and head pose estimation.”
Facial Expression Analysis (2011, methodology): “In automated facial image analysis, investigators typically assume exact agrement between classifiers and ground truth, which is a level of temporal precision beyond what may be feasible for many AU [24].”
Development and application of CatFACS: Are human cat adopters influenced by cat facial expressions? (2017, methodology): “FACS bases its coding of facial movements on the muscular activity instead of the traditional emotion labelling system, by assigning independent codes (Action Units) to each facial muscle contraction (Ekman et al., 2002; Cohn et al., 2007).”
Face Expression Recognition and Analysis: The State of the Art (2012, background): “They may give more prominence to the voice rather than the face and furthermore, the observations made may not be the same across cultures; different cultural groups may have different interpretations [29].”
Going deeper in facial expression recognition using deep neural networks (2015, background): “[6, 12] distinguished two conceptual approaches to studying facial behavior: a “message-based” approach and a “sign-based” approach.”