Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

Kangkang Zhu; Yunhong Wang; Hongyu Yang; Di Huang; Liming Chen

doi:10.1109/ICIP40778.2020.9190705

Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

Kangkang Zhu
, Yunhong Wang
, Hongyu Yang
, Di Huang
, Liming Chen

Beihang University

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

9 Scopus citations

Abstract

Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.

Original language	English
Title of host publication	2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings
Publisher	IEEE Computer Society
Pages	1346-1350
Number of pages	5
ISBN (Electronic)	9781728163956
DOIs	https://doi.org/10.1109/ICIP40778.2020.9190705
State	Published - Oct 2020
Event	2020 IEEE International Conference on Image Processing, ICIP 2020 - Virtual, Abu Dhabi, United Arab Emirates Duration: 25 Sep 2020 → 28 Sep 2020

Publication series

Name	Proceedings - International Conference on Image Processing, ICIP
Volume	2020-October
ISSN (Print)	1522-4880

Conference

Conference	2020 IEEE International Conference on Image Processing, ICIP 2020
Country/Territory	United Arab Emirates
City	Virtual, Abu Dhabi
Period	25/09/20 → 28/09/20

Keywords

Face Expression Recognition
Generative Adversarial Network
Intensity Enhancement

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10.1109/ICIP40778.2020.9190705

Cite this

Zhu, K., Wang, Y., Yang, H., Huang, D., & Chen, L. (2020). Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition. In 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings (pp. 1346-1350). Article 9190705 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2020-October). IEEE Computer Society. https://doi.org/10.1109/ICIP40778.2020.9190705

@inproceedings{d08eb96a31ed481786209810468e9dfe,

title = "Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition",

abstract = "Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.",

keywords = "Face Expression Recognition, Generative Adversarial Network, Intensity Enhancement",

author = "Kangkang Zhu and Yunhong Wang and Hongyu Yang and Di Huang and Liming Chen",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Image Processing, ICIP 2020 ; Conference date: 25-09-2020 Through 28-09-2020",

year = "2020",

month = oct,

doi = "10.1109/ICIP40778.2020.9190705",

language = "英语",

series = "Proceedings - International Conference on Image Processing, ICIP",

publisher = "IEEE Computer Society",

pages = "1346--1350",

booktitle = "2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings",

address = "美国",

}

Zhu, K, Wang, Y , Yang, H , Huang, D & Chen, L 2020, Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition. in 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings., 9190705, Proceedings - International Conference on Image Processing, ICIP, vol. 2020-October, IEEE Computer Society, pp. 1346-1350, 2020 IEEE International Conference on Image Processing, ICIP 2020, Virtual, Abu Dhabi, United Arab Emirates, 25/09/20. https://doi.org/10.1109/ICIP40778.2020.9190705

Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition. / Zhu, Kangkang; Wang, Yunhong ; Yang, Hongyu et al.
2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings. IEEE Computer Society, 2020. p. 1346-1350 9190705 (Proceedings - International Conference on Image Processing, ICIP; Vol. 2020-October).

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

TY - GEN

T1 - Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

AU - Zhu, Kangkang

AU - Wang, Yunhong

AU - Yang, Hongyu

AU - Huang, Di

AU - Chen, Liming

PY - 2020/10

Y1 - 2020/10

N2 - Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.

AB - Face expression recognition (FER) on low intensity is not well studied in the literature. This paper investigates this new problem and presents a novel Generative Adversarial Network (GAN) based multimodal approach to it. The method models the tasks of intensity enhancement and expression recognition jointly, ensuring that the synthesize faces not only present expression of high intensity, but also truly contribute to promoting the performance of FER. Extensive experiments are conducted on the BU-3DFE and BU-4DFE datasets. State-of-the-art FER performance clearly validates the effectiveness of the proposed method.

KW - Face Expression Recognition

KW - Generative Adversarial Network

KW - Intensity Enhancement

UR - https://www.scopus.com/pages/publications/85098627981

U2 - 10.1109/ICIP40778.2020.9190705

DO - 10.1109/ICIP40778.2020.9190705

M3 - 会议稿件

AN - SCOPUS:85098627981

T3 - Proceedings - International Conference on Image Processing, ICIP

SP - 1346

EP - 1350

BT - 2020 IEEE International Conference on Image Processing, ICIP 2020 - Proceedings

PB - IEEE Computer Society

T2 - 2020 IEEE International Conference on Image Processing, ICIP 2020

Y2 - 25 September 2020 through 28 September 2020

ER -

Intensity Enhancement Via Gan for Multimodal Facial Expression Recognition

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