Automatic identification of head movements in video-recorded conversations: can words help?
Open accessAbstract
We present an approach where an SVM classifier learns to classify head movements based on measurements of velocity, acceleration, and the third derivative of position with respect to time, jerk. Consequently, annotations of head movements are added to new video data. The results of the automatic annotation are evaluated against manual annotations in the same data and show an accuracy of 68% with respect to these. The results also show that using jerk improves accuracy. We then conduct an investigation of the overlap between temporal sequences classified as either movement or non-movement and the speech stream of the person performing the gesture. The statistics derived from this analysis show that using word features may help increase the accuracy of the model.
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Paggio et al. (2017) were only able to achieve a 68% accuracy in classifying the presence of any head gesture.
“Paggio et al. (2017) attempted to classify the presence of any head gesture (from frame-to-frame movement features) in a video corpus but were only able to achieve a 68% accuracy.”
From Modeling Feedback in Interaction With Conversational Agents—A Review (2022) · cites this paper as Paggio et al., 2017
Cites
Contextual recognition of head gestures,
Morency, L.-P., Sidner, C., Lee, C., & Darrell, T. (2005) · Proceedings of the 7th international conference on Multimodal interfaces · doi:10.1145/1088463.1088470 · Cited by 130 · see citing works
Searching for prototypical facial feedback signals,
Heylen, D., Bevacqua, E., Tellier, M., & Pelachaud, C. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-74997-4_14 · Cited by 41 · see citing works
Generating Robot/Agent backchannels during a storytelling experiment
Al Moubayed, S., Baklouti, M., Chetouani, M., Dutoit, T., Mahdhaoui, A., Martin, J.-C., Ondas, S., Pelachaud, C., Urbain, J., & Yilmaz, M. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152572 · Cited by 33 · see citing works
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Modeling Feedback in Interaction With Conversational Agents—A Review
Axelsson, A., Buschmeier, H., & Skantze, G. (2022) · Frontiers in Computer Science · doi:10.3389/fcomp.2022.744574 · Cited by 33 · see citing works
“Paggio et al. (2017) attempted to classify the presence of any head gesture (from frame-to-frame movement features) in a video corpus but were only able to achieve a 68% accuracy.”
Background
- Authors
- Paggio, P., Navarretta, C., & Jongejan, B.
- Affiliations
- University of Copenhagen (DK)
- Venue
- Proceedings of the Sixth Workshop on Vision and Language
- Published
- 2017
- Pages
- 40-42
- OpenAlex
- W2740655761
- Cited by
- 4
- References
- 3
- Citations by year
- 2023: 2 · 2022: 1 · 2021: 1
- FWCI
- 0.00
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on Vision and Language (VL) (publishedVersion, cc-by)
- Research at the University of Copenhagen (University of Copenhagen) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Hand Gesture Recognition Systems
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
- Keywords
- head movement detection, video annotation, support vector machine, jerk, acceleration, velocity, word features, conversational video, automatic annotation
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