Transactions on Affective Computing

IEEE Transactions on Affective Computing (TAC) is intended to be a cross disciplinary and international archive journal aimed at disseminating results of research on the design of systems that can recognize, interpret, and simulate human emotions and related affective phenomena. Read the full scope of TAC


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From the January-March 2017 issue

Automated Detection of Engagement Using Video-Based Estimation of Facial Expressions and Heart Rate

By Hamed Monkaresi, Nigel Bosch, Rafael A. Calvo, and Sidney K. D’Mello

Featured article thumbnail imageWe explored how computer vision techniques can be used to detect engagement while students (N = 22) completed a structured writing activity (draft-feedback-review) similar to activities encountered in educational settings. Students provided engagement annotations both concurrently during the writing activity and retrospectively from videos of their faces after the activity. We used computer vision techniques to extract three sets of features from videos, heart rate, Animation Units (from Microsoft Kinect Face Tracker), and local binary patterns in three orthogonal planes (LBP-TOP). These features were used in supervised learning for detection of concurrent and retrospective self-reported engagement. Area under the ROC Curve (AUC) was used to evaluate classifier accuracy using leave-several-students-out cross validation. We achieved an AUC = .758 for concurrent annotations and AUC = .733 for retrospective annotations. The Kinect Face Tracker features produced the best results among the individual channels, but the overall best results were found using a fusion of channels.

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