Fully Automatic Facial Action Recognition in Spontaneous Behavior

By M. Bartlett, Gwen Littlewort, Mark G. Frank, Claudia Lainscsek, Ian R. Fasel, Javier R. Movellan. International Conference on Automatic Face and Gesture Recognition, 2006

346 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/8b096a7bb0c471074c9a48383052b7ccd7b8e515

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Citations in the corpus

Aff-Wild: Valence and Arousal ‘In-the-Wild’ Challenge (2017, methodology): “Examples include the recently collected EmoPain [4] and UNBC-McMaster [27] for analysis of pain, the RU-FACS database consisting of subjects participating in a false opinion scenario [5] and the SEMAINE [27] corpus which contains recordings of subjects interacting with a Sensitive Artificial Listene”
The first facial expression recognition and analysis challenge (2011, background): “[5], [2]), those that use geometric feature-based approaches (e.”
Deep Affect Prediction in-the-Wild: Aff-Wild Database and Challenge, Deep Architectures, and Beyond (2018, methodology): “Examples include the recently collected EmoPain [3] and UNBC-McMaster [33] databases for analysis of pain, the RU-FACS database of subjects participating in a false opinion scenario [4] and the SEMAINE corpus [36] which contains recordings of subjects interacting with a Sensitive Artificial Listener”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “Other available facial expression recognition systems include [110], which has also been used as an aid for autistic children, as well as [111], and [112], where the output of the system is at the level of facial action coding (FACS).”
Robust semantic analysis by synthesis of 3D facial motion (2011, background): “In contrast, 3D part-based approaches receive growing attention for 3D face feature tracking [6], and for direct 3D expression recognition purposes [8], but also for 2D recognition and automatic facial behavior recognition from video, based on Facial Action Units [18].”
Facial Expression Analysis (2011, methodology): “[69, 7, 8] used Gabor filters in conjunction with AdaBoost feature selection followed by a Support Vector Machine (SVM) classifier.”
Human computing and machine understanding of human behavior: a survey (2006, background): “Few exceptions from this overall state of the art in the field include a few tentative efforts to detect attitudinal and non-basic affective states such as boredom, fatigue, and pain from face video [4], a few works on context-sensitive interpretation of behavioral cues like facial expressions [60],”