Abstract
Machine learning-based approaches have greatly accelerated the progresses in the field of micro-expression detection and recognition. However, many models, especially those based on deep learning require very large databases of hand labeled data for training. Existing micro-expression data collection processes usually cost a lot and are time-consuming. The labeling of very large datasets is becoming a bottleneck of building robust models for micro-expression detection and recognition. With the wide success of crowdsourcing platforms in creating large datasets like ImageNet, in this paper, we present Crowd4ME. Crowd4ME is a crowdsourcing-based platform for collecting large scale micro-expression data. We demonstrate that using Crowd4ME can help collect and manage micro expression samples more easily and efficiently.
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References
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Acknowledgements
This work was supported in part Supported by the National Key Research and Development Program of China (2018YFB1404102), the National Natural Science Foundation of China (61976187, 92046002 and 61976188) and the Research Program of Zhejiang Lab (2019KD0AC02).
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Wang, X., Lv, L., Fang, Y., Ding, X., Han, T. (2022). Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2021 Workshops. ICSOC 2021. Lecture Notes in Computer Science, vol 13236. Springer, Cham. https://doi.org/10.1007/978-3-031-14135-5_28
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DOI: https://doi.org/10.1007/978-3-031-14135-5_28
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