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Abstract
The shortage of emotion-annotated video datasets suitable for training and validating machine learning models for facial expression-based emotion recognition stems primarily from the significant effort and cost required for manual annotation. In this paper, we present AffecTube as a comprehensive solution that leverages crowdsourcing to annotate videos directly on the YouTube platform, resulting in ready-to-use emotion-annotated datasets. AffecTube provides a low-resource environment with an intuitive interface and customizable options, making it a versatile tool applicable not only to emotion annotation, but also to various video-based behavioral annotation processes.
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- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.softx.2023.101504
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- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
-
SoftwareX
no. 23,
ISSN: - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Kulas D., Wróbel M.: AffecTube — Chrome extension for YouTube video affective annotations// SoftwareX -,iss. 23 (2023), s.=-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.softx.2023.101504
- Sources of funding:
-
- IDUB
- Verified by:
- Gdańsk University of Technology
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