Skip to main content

Crowd4ME: A Crowdsourcing-Based Micro-expression Collection Platform

  • Conference paper
  • First Online:
Service-Oriented Computing – ICSOC 2021 Workshops (ICSOC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13236))

Included in the following conference series:

  • 693 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Shan, C., Mamoulis, N., Li, G., et al.: A crowdsourcing framework for collecting tabular data. IEEE Trans. Knowl. Data Eng. 32(11), 2060–2074 (2019)

    Article  Google Scholar 

  2. Felizardo, K.R., de Souza É.F., Lopes, R., et al.: Crowdsourcing in systematic reviews: a systematic mapping and survey. In: 2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA), pp. 404–412. IEEE (2020)

    Google Scholar 

  3. He, Y., Wang, S.J., Li, J., et al.: Spotting macro-and micro-expression intervals in long video sequences. In: 2020 15th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2020), pp. 742–748. IEEE (2020)

    Google Scholar 

Download references

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).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tao Han .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

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

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-14135-5_28

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-14134-8

  • Online ISBN: 978-3-031-14135-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics