GAN-based Algorithm for Efficient Image Inpainting
3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022
; 12610, 2023.
Article
in English
| Scopus | ID: covidwho-2323482
ABSTRACT
Global pandemic due to the spread of COVID-19 has post challenges in a new dimension on facial recognition, where people start to wear masks. Under such condition, the authors consider utilizing machine learning in image inpainting to tackle the problem, by complete the possible face that is originally covered in mask. In particular, autoencoder has great potential on retaining important, general features of the image as well as the generative power of the generative adversarial network (GAN). The authors implement a combination of the two models, context encoders and explain how it combines the power of the two models and train the model with 50,000 images of influencers faces and yields a solid result that still contains space for improvements. Furthermore, the authors discuss some shortcomings with the model, their possible improvements, as well as some area of study for future investigation for applicative perspective, as well as directions to further enhance and refine the model. © 2023 SPIE.
autoencoder; Generative Adversarial Network (GAN); Image inpainting; Face recognition; Image enhancement; Learning systems; Auto encoders; Condition; Facial recognition; Generative adversarial network; Machine-learning; Model contexts; Network-based algorithm; New dimensions; Power; Generative adversarial networks
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
3rd International Conference on Artificial Intelligence and Computer Engineering, ICAICE 2022
Year:
2023
Document Type:
Article
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