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Open Access

Correction

Correction: Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity

  • The PLOS ONE Staff

There are errors in the Funding statement. The publisher apologizes for the errors. The correct Funding statement is as follows: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, & Future Planning (2018R1C1B3007313).

Reference

  1. 1. Haines N, Southward MW, Cheavens JS, Beauchaine T, Ahn W- Y (2019) Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS ONE 14(2): e0211735. https://doi.org/10.1371/journal.pone.0211735 pmid:30721270

Citation: The PLOS ONE Staff (2019) Correction: Using computer-vision and machine learning to automate facial coding of positive and negative affect intensity. PLoS ONE 14(3): e0213756. https://doi.org/10.1371/journal.pone.0213756

Published: March 7, 2019

Copyright: © 2019 The PLOS ONE Staff. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.