The SEMAINE corpus of emotionally coloured character interactions

By Gary McKeown, Michel François Valstar, R. Cowie, Maja Pantic. IEEE International Conference on Multimedia and Expo, 2010

322 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Sensory Systems, Social Psychology

View Paper: https://www.semanticscholar.org/paper/6240ce60e12b817ad8e4f660386ded68225f18d5

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In Experimental and Cognitive Psychology

In Sensory Systems

In Social Psychology

Citations in the corpus

Categorical and dimensional affect analysis in continuous input: Current trends and future directions (2013, unknown): “, [23]).”
Induced Disgust , Happiness and Surprise : an Addition to the MMI Facial Expression Database (2010, background): “, 2007), SEMAINE (McKeown et al., 2010), and Spaghetti (Douglas-Cowie et al.”
Multimodal fusion framework: A multiresolution approach for emotion classification and recognition from physiological signals (2014, methodology): “, 2008), SEMAINE (McKeown et al., 2010), MAHNOB-HCI (Soleymani et al.”
A Multimodal Database for Affect Recognition and Implicit Tagging (2012, background): “These databases mostly include speech, visual, or audio-visual data [5], [6], [7], [8].”
OpenFace: An open source facial behavior analysis toolkit (2016, methodology): “Our models are trained on DISFA [41], SEMAINE [44] and BP4D [69] datasets.”
Towards multimodal sentiment analysis: harvesting opinions from the web (2011, methodology): “Further, most sentiment analysis datasets are created by the researchers in the scientific environment with accurate precision recording and reduced noises [6, 4, 11].”
Cross-dataset learning and person-specific normalisation for automatic Action Unit detection (2015, methodology): “In our experiments, we mainly used three datasets: DISFA [16], BP4D-Spontaneous [27] and SEMAINE [18].”
Continuous Prediction of Spontaneous Affect from Multiple Cues and Modalities in Valence-Arousal Space (2011, background): “As future work, the proposed methodology remains to be evaluated on extensive data sets (with a larger number of subjects) annotated using a richer emotional expression space with other continuous dimensions such as power, expectation, and intensity (e.g., the newly released Semaine Database [43]).”
OpenFace 2.0: Facial Behavior Analysis Toolkit (2018, methodology): “Our models are trained on DISFA [48], SEMAINE [51], BP4D [80], UNBC-McMaster [44], Bosphorus [59] and FERA 2011 [66] datasets.”
Collecting Large, Richly Annotated Facial-Expression Databases from Movies (2012, background): “C Yes 10 No No No M-PIE4 Manual Lab 27.9 C Yes 337 No No No MMI5 Manual Lab 19 62 C Yes 29 Yes No No Paleari11 Manual CTR CTN Yes No No No RU-FACS6 Manual Lab 18 30 C Yes 100 No No No Semaine12 Manual Lab ?”
Emotion representation, analysis and synthesis in continuous space: A survey (2011, background): “The SEMAINE corpus [9] consists of naturalistic audio-visual conversations taking place between a participant and a number of virtual characters with particular personalities.”
Image based Static Facial Expression Recognition with Multiple Deep Network Learning (2015, background): “Categories and Subject Descriptors I.5.4 [Pattern Recognition]: Applications—computer vision, signal processing ; I.4.m [Image Processing and Computer Vision]: Miscellaneous Keywords Facial Expression Recognition; Convolutional Neural Network; Multiple Network Learning; EmotiW 2015 Challenge”
AVEC 2011-The First International Audio/Visual Emotion Challenge (2011, background): “The SEMAINE corpus [11], freely available for scientific research purposes from http://semaine-db.”