Real-Time Inference of Complex Mental States from Facial Expressions and Head Gestures

By Rana El Kaliouby, Peter Robinson. 2004 Conference on Computer Vision and Pattern Recognition Workshop, 2004

389 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ba40009375fb2cae3490e7a895929df72e56440c

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In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Emotion Research by the People, for the People (2010, methodology): “…models—used to teach computers skills such as speech recognition—are increasingly used by engineers for emotion recognition, including facial expressions, prosody, and stereotypical gestures (Albinali, Goodwin, & Intille, 2009; Fernandez, 2004; el Kaliouby & Robinson, 2005; Tong, Liao, & Ji, 2007).”
It Takes Two to Tango: Towards Theory of AI's Mind (2017, background): “However, AI research has traditionally placed much of the burden on the AI to play its part in the team: to be more accurate [32, 56, 65, 57, 39], more human-like [4, 36, 48, 23, 3, 54, 50, 10, 13], understand our intentions [49, 67, 68], beliefs [22], tendencies [18], contexts [55], and mental stat”
Multimodal Human Computer Interaction: A Survey (2005, background): “The most surprising issue regarding the multimodal affect recognition problem is that although recent advances in video and audio processing could make the multimodal analysis of human affective state tractable, there are only a few research efforts [80][159][153][195][157] that have tried to implem”
Remote measurement of cognitive stress via heart rate variability (2014, background): “Although nonverbal cues such as facial expressions can be useful in detecting cognitive states (such as concentration) [6], in many cases people do not exhibit facial muscle movements.”
Fully Automatic Recognition of the Temporal Phases of Facial Actions (2012, neutral): “[17], pain [18], [19], and fatigue [20].”
MACH: my automated conversation coach (2013, methodology): “[3] developed a real-time system called MindReader to recognize and visualize comple x mental states by analyzing facial expressions and displaying the inferred states using radial charts, t affic lights, and lines that change in real-time.”
Human computing and machine understanding of human behavior: a survey (2006, background): “Other important works in the field include efforts towards analysis of interest, agreement and disagreement from facial and head movements [18] and towards analysis of the level of interest from tone of voice, head and hand movements [21].”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “Peter Robinson and his team have worked on the use of facial expressions for affect detection [74], [75].”
Facial expression recognition based on Local Binary Patterns: A comprehensive study (2009, methodology): “Kaliouby and Robinson [16] proposed a system for inferring complex mental states from videos of facial expressions and head gestures, where a multi-level DBN classiﬁer was used to model complex mental states as a number of interacting facial and head displays.”
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).”
Affective Body Expression Perception and Recognition: A Survey (2013, background): “More recently, however, in 2007, while still employing a posture sensing chair, head position (shown by [40], [80], [134] to be an important feature for discriminating between affective states) and velocity were added to the list of features for the system built to recognize learner frustration [22]”
Facial Action Unit Recognition by Exploiting Their Dynamic and Semantic Relationships (2007, methodology): “The approaches on AU classification could be grouped into spatial approaches [17], [3], [4], [10], [7], [5], [6], [11], [12], [16], [8], [13], [18], where AUs are analyzed frame by frame, and spatial-temporal approaches [3], [9], [14], [15], [19], where AUs are recognized over time based on the…”