Micro expression classification using facial color and deep learning methods

Hadas Shahar; Hagit Hel-Or

doi:10.1109/ICCVW.2019.00207

Micro expression classification using facial color and deep learning methods

Hadas Shahar, Hagit Hel-Or

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Micro emotions are a unique type of facial expression as they are involuntary and very brief. They usually occur when a person attempts to suppress or hide their emotions. Lasting less than 500 ms, they can be very hard to detect even for the human eye, however, since they reveal a person's true feelings, they are of extreme interest in many fields of study. Most approaches to automatic detection and classification of micro emotions rely on detecting the small residual facial movements. In this paper, we propose to exploit an aspect of the human face which is much harder to subdue, namely, facial color change due to blood flow during expression of emotion. We propose a system that evaluates color change during micro emotion expression and successfully classifies the emotion type. This approach is unique in that it disregards the motion related aspects of the expression, and relies entirely on the facial color. We show that our system improves over movement based approaches.

Original language	English
Title of host publication	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1673-1680
Number of pages	8
ISBN (Electronic)	9781728150239
DOIs	https://doi.org/10.1109/ICCVW.2019.00207
State	Published - Oct 2019
Event	17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 - Seoul, Korea, Republic of Duration: 27 Oct 2019 → 28 Oct 2019

Publication series

Name	Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019

Conference

Conference	17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019
Country/Territory	Korea, Republic of
City	Seoul
Period	27/10/19 → 28/10/19

Bibliographical note

Funding Information:
This research was supported by grant no 1455/16 from the Israeli Science Foundation.

Publisher Copyright:
© 2019 IEEE.

Keywords

Deep learning
Facial color
Micro emotion
Micro expression

ASJC Scopus subject areas

Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/ICCVW.2019.00207

Cite this

Shahar, H., & Hel-Or, H. (2019). Micro expression classification using facial color and deep learning methods. In Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019 (pp. 1673-1680). Article 9021953 (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCVW.2019.00207

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title = "Micro expression classification using facial color and deep learning methods",

abstract = "Micro emotions are a unique type of facial expression as they are involuntary and very brief. They usually occur when a person attempts to suppress or hide their emotions. Lasting less than 500 ms, they can be very hard to detect even for the human eye, however, since they reveal a person's true feelings, they are of extreme interest in many fields of study. Most approaches to automatic detection and classification of micro emotions rely on detecting the small residual facial movements. In this paper, we propose to exploit an aspect of the human face which is much harder to subdue, namely, facial color change due to blood flow during expression of emotion. We propose a system that evaluates color change during micro emotion expression and successfully classifies the emotion type. This approach is unique in that it disregards the motion related aspects of the expression, and relies entirely on the facial color. We show that our system improves over movement based approaches.",

keywords = "Deep learning, Facial color, Micro emotion, Micro expression",

author = "Hadas Shahar and Hagit Hel-Or",

note = "Funding Information: This research was supported by grant no 1455/16 from the Israeli Science Foundation. Publisher Copyright: {\textcopyright} 2019 IEEE.; 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019 ; Conference date: 27-10-2019 Through 28-10-2019",

year = "2019",

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doi = "10.1109/ICCVW.2019.00207",

language = "English",

series = "Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019",

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Shahar, H & Hel-Or, H 2019, Micro expression classification using facial color and deep learning methods. in Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019., 9021953, Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019, Institute of Electrical and Electronics Engineers Inc., pp. 1673-1680, 17th IEEE/CVF International Conference on Computer Vision Workshop, ICCVW 2019, Seoul, Korea, Republic of, 27/10/19. https://doi.org/10.1109/ICCVW.2019.00207

Micro expression classification using facial color and deep learning methods. / Shahar, Hadas; Hel-Or, Hagit.
Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 1673-1680 9021953 (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Shahar H, Hel-Or H. Micro expression classification using facial color and deep learning methods. In Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 1673-1680. 9021953. (Proceedings - 2019 International Conference on Computer Vision Workshop, ICCVW 2019). doi: 10.1109/ICCVW.2019.00207

Micro expression classification using facial color and deep learning methods

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this