Domain adaptation based topic modeling techniques for engagement estimation in the wild

Amanjot Kaur, Bishal Ghosh, Naman Deep Singh, Abhinav Dhall

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch


In recent years, student engagement estimation has gained focus in the affective computing community. The absence of student monitoring during online MOOC courses makes it challenging to estimate behavioural student engagement during online classes. The non availability of consistent engagement datasets makes it difficult to build cross data automatic behavioural engagement estimation technique. In this paper, we propose an unsupervised topic modeling technique for engagement detection as it captures multiple behavioral cues which are indicators of engagement level such as eye gaze, head movement, facial expression and body posture. We have addressed the various challenges such as less volume of our datasets, large decision unit (annotated for 5 minutes duration) and uneven distribution of different engagement categories with domain adaptation based solution for cross data implementation. We present results on engagement prediction using different clustering techniques such as K-Means and Latent Dirichlet Allocation (LDA) along with different regressors and neural network based attention mechanisms.

Original languageEnglish
Title of host publicationProceedings - 14th IEEE International Conference on Automatic Face and Gesture Recognition, FG 2019
EditorsPeter Hancock, Richa Singh, Catherine Pelachaud, Vassilis Athitsos
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)9781728100890, 9781728100883
ISBN (Print)9781728100906
Publication statusPublished - 2019
Externally publishedYes
EventIEEE International Conference on Automatic Face and Gesture Recognition 2019 - Lille, France
Duration: 14 May 201918 May 2019
Conference number: 14th


ConferenceIEEE International Conference on Automatic Face and Gesture Recognition 2019
Abbreviated titleFG 2019
Internet address

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