Multimodal affect recognition in learning environments

By Ashish Kapoor, Rosalind W. Picard. ACM Multimedia, 2005

380 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/c0bfacb4d16f7d2aebb725ea252bebd56420b8e4

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computer Vision and Pattern Recognition

In Experimental and Cognitive Psychology

Citations in the corpus

Automatic prediction of frustration (2007, methodology): “Research incorporating the posture analysis seat (Mota and Picard, 2003) and the Blue-Eyes camera (Haro et al., 2000) has classiﬁed engagement, boredom and break-taking behavior with 86% accuracy (Kapoor and Picard, 2005).”
Multimodal semi-automated affect detection from conversational cues, gross body language, and facial features (2010, background): “…probabilistic system to infer a child’s interest level on the basis of upper and lower facial feature tracking, posture patterns (current posture and level of activity), and some contextual information (difficulty level and state of the game) (Burleson and Picard 2007; Kapoor and Picard 2005).”
Towards an automatic early stress recognition system for office environments based on multimodal measurements: A review (2016, background): “It has been proven that posture is a good indicator about the feelings of the worker towards the tasks they are carrying out [100].”
Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications (2010, background): “On the more naturalistic front, Kapoor and Picard developed a contextually grounded probabilistic system to infer a child’s interest level on the basis of upper and lower facial feature tracking, posture patterns (current posture and level of activity), and some contextual information (difficulty le”
Emotion recognition using a hierarchical binary decision tree approach (2011, background): “For example, there are increasing numbers of interactive educational systems commercially available and under development [3, 4].”
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions (2014, methodology): “Our work extends prior research on engagement recognition using computer vision [10], [11], [29], [42] and is arguably the most thorough study on this topic to date: We collected a data set of student facial expressions while performing a cognitive training task.”
AffectAura: an intelligent system for emotional memory (2012, background): “Kapoor and Picard [21] developed one of the first multimodal affect recognition systems, predicting student interest in a learning environment.”
Empirically building and evaluating a probabilistic model of user affect (2009, methodology): “In contrast, most work on affect recognition has focused on detecting one specific emotion (e.g., Kapoor and Picard 2005; Healey and Picard 2005), lower-level affective measures such as affective valence and arousal (e.g., Prendinger et al. 2005; Litman and Forbes-Riley 2004) or overall emotional…”
A Review and Meta-Analysis of Multimodal Affect Detection Systems (2015, background): “[Kapoor and Picard 2005] 8 nat disc class 2 disc nonbasic Face + Body + Content model dep”
Approximations for Binary Gaussian Process Classification (2008, methodology): “, 2005), EP for affect recognition (Kapoor and Picard, 2005), VB for weld cracking prognosis (Gibbs and MacKay, 2000), Label Regression (LR) serves for object categorization (Kapoor et al.”