Realistic Face Masks Generation Using Generative Adversarial Networks

Khaled Al Butainy; Muhamad Felemban; Hamzah Luqman

doi:10.1109/CICN56167.2022.10008304

Realistic Face Masks Generation Using Generative Adversarial Networks

Khaled Al Butainy, Muhamad Felemban, Hamzah Luqman

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

1 Scopus citations

Abstract

Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.

Original language	English
Title of host publication	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	90-95
Number of pages	6
ISBN (Electronic)	9781665487719
DOIs	https://doi.org/10.1109/CICN56167.2022.10008304
State	Published - 2022
Event	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 - Al-Khobar, Saudi Arabia Duration: 4 Dec 2022 → 6 Dec 2022

Publication series

Name	Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022

Conference

Conference	14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Country/Territory	Saudi Arabia
City	Al-Khobar
Period	4/12/22 → 6/12/22

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

Keywords

CelebA
Emotion detection
Generative Adversarial Networks
Image inpainting
Learning-based models
MAFA

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/CICN56167.2022.10008304

Cite this

Butainy, K. A., Felemban, M., & Luqman, H. (2022). Realistic Face Masks Generation Using Generative Adversarial Networks. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022 (pp. 90-95). (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CICN56167.2022.10008304

Butainy, Khaled Al ; Felemban, Muhamad ; Luqman, Hamzah. / Realistic Face Masks Generation Using Generative Adversarial Networks. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 90-95 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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title = "Realistic Face Masks Generation Using Generative Adversarial Networks",

abstract = "Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.",

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author = "Butainy, {Khaled Al} and Muhamad Felemban and Hamzah Luqman",

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year = "2022",

doi = "10.1109/CICN56167.2022.10008304",

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Butainy, KA, Felemban, M & Luqman, H 2022, Realistic Face Masks Generation Using Generative Adversarial Networks. in Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Institute of Electrical and Electronics Engineers Inc., pp. 90-95, 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022, Al-Khobar, Saudi Arabia, 4/12/22. https://doi.org/10.1109/CICN56167.2022.10008304

Realistic Face Masks Generation Using Generative Adversarial Networks. / Butainy, Khaled Al; Felemban, Muhamad ; Luqman, Hamzah.
Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 90-95 (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022).

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

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AU - Felemban, Muhamad

AU - Luqman, Hamzah

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N2 - Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.

AB - Understanding facial expressions is important for the interactions among humans as it conveys a lot about the person's identity and emotions. Research in human emotion recognition has become more popular nowadays due to the advances in the machine learning and deep learning techniques. However, the spread of COVID-19, and the need for wearing masks in the public has impacted the current emotion recognition models' performance. Therefore, improving the performance of these models requires datasets with masked faces. In this paper, we propose a model to generate realistic face masks using generative adversarial network models, in particular image inpainting. The MAFA dataset was used to train the generative image inpainting model. In addition, a face detection model was proposed to identify the mask area. The model was evaluated using the MAFA and CelebA datasets, and promising results were obtained.

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Butainy KA, Felemban M , Luqman H. Realistic Face Masks Generation Using Generative Adversarial Networks. In Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 90-95. (Proceedings - 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022). doi: 10.1109/CICN56167.2022.10008304

Realistic Face Masks Generation Using Generative Adversarial Networks

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this