University of Oulu

Varanka, Tuomas; Peng, Wei; Zhao, Guoying (2021) Micro-expression recognition with noisy labels. In Chandler D.; McCourt M.; Mulligan J. (eds.) Human Vision and Electronic Imaging 2021, Held at IS&T International Symposium on Electronic Imaging Science and Technology 2021, (pp. 157-1-157-8). Society for imaging science and technology.

Micro-expression recognition with noisy labels

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Author: Varanka, Tuomas1; Peng, Wei1; Zhao, Guoying1
Organizations: 1Center for Machine Vision and Signal Analysis, University of Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.7 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe202201144837
Language: English
Published: Society for Imaging Science & Technology, 2021
Publish Date: 2022-01-14
Description:

Abstract

Facial micro-expressions are quick, involuntary and low intensity facial movements. An interest in detecting and recognizing micro-expressions arises from the fact that they are able to show person’s genuine hidden emotions. The small and rapid facial muscle movements are often too difficult for a human to not only spot the occurring micro-expression but also be able to recognize the emotion correctly. Recently, a focus on developing better micro-expression recognition methods has been on models and architectures. However, we take a step back and go to the root of task, the data.

We thoroughly analyze the input data and notice that some of the data is noisy and possibly mislabelled. The authors of the micro-expression datasets have also acknowledged the possible problems in data labelling. Despite this, no attempts have been made to design models that take into account the potential mislabelled data in micro-expression recognition, to our best knowledge. In this paper, we explore new methods taking noisy labels into special account in an attempt to solve the problem. We propose a simple, yet efficient label refurbishing method and a data cleaning method for handling noisy labels. The data cleaning method achieves state-of-the-art results in the MEGC2019 composite dataset.

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Series: IS&T International Symposium on Electronic Imaging
ISSN: 2470-1173
ISSN-E: 2470-1173
ISSN-L: 2470-1173
Pages: 1 - 8
Article number: 157
Host publication: Human Vision and Electronic Imaging 2021, Held at IS&T International Symposium on Electronic Imaging Science and Technology 2021
Host publication editor: Chandler, D.
McCourt, M.
Mulligan, J.
Conference: Human Vision and Electronic Imaging
Type of Publication: A4 Article in conference proceedings
Field of Science: 222 Other engineering and technologies
Subjects:
Funding: This work was supported by the Academy of Finland for ICT 2023 project (grant 328115), project MiGA (grant 316765), and Infotech Oulu. As well, the authors wish to acknowledge CSC – IT Center for Science, Finland, for computational resources.
Academy of Finland Grant Number: 328115
316765
Detailed Information: 328115 (Academy of Finland Funding decision)
316765 (Academy of Finland Funding decision)
Copyright information: © 2021, Society for Imaging Science and Technology.