Realistic Face Masks Generation Using Generative Adversarial Networks
14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
; : 90-95, 2022.
Article
in English
| Scopus | ID: covidwho-2228461
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. © 2022 IEEE.
CelebA; Emotion detection; Generative Adversarial Networks; Image inpainting; Learning-based models; MAFA; Computer vision; Deep learning; Face recognition; Learning systems; Speech recognition; Face masks; Facial Expressions; Human emotion recognition; Learning Based Models; Learning techniques; Machine-learning; Emotion Recognition
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Language:
English
Journal:
14th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2022
Year:
2022
Document Type:
Article
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