Research: Facial wrinkles detection and inpainting
Improving the realism and experience of virtual try-on technology for cosmetic and medical products.
Research summary
Research summary
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2016 to 2022
This project seeks to improve the experience of trying products out virtually using augmented reality.
Virtual try-on for faces is popular in gaming and cosmetics, but can also be applied in medical practices such as facial reconstruction surgery and medicinal skin products.
We are developing a new technique that can fill in missing parts of the facial image through inpainting in real-time to create a more realistic appearance. Our solution uses machine intelligence to integrate facial wrinkling information that can be used on a mobile platform with a quicker processing time than existing applications.
Working alongside face-tracking and AR specialists Image Metrics, we are testing our method in real-world applications.
This research will investigate deep learning architectures for various inpainting tasks.
Research outputs
Selected academic papers
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Yap, MH, Batool, N, Ng, CC, Rogers, M and Walker, K (2021) A Survey on Facial Wrinkles Detection and Inpainting: Datasets, Methods, and Challenges IEEE Transactions on Emerging Topics in Computational Intelligence
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Jam, J, Kendrick, C, Drouard, V, Walker, K, Hsu, GS and Yap, MH (2021) R-MNET: A perceptual adversarial network for image inpainting, Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp 2714-2723
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Jam, J, Kendrick, C, Drouard, V, Walker, K, Hsu, GS and Yap, MH (2020) A comprehensive review of past and present image inpainting methods, Computer Vision and Image Understanding, p.103147
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Tang, CH, Hsu, GSJ and Yap, MH (2019) Face Recognition with Disentangled Facial Representation Learning and Data Augmentation IEEE International Conference on Image Processing (ICIP), pp 1670-1674
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Yap, MH, Alarifi, JS, Ng, CC, Batool, N and Walker, K (2018) Automated Facial Wrinkles Annotator ECCV Workshops, 4,pp 676-680
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Osman, OF and Yap, MH (2018) Computational intelligence in automatic face age estimation: A survey IEEE Transactions on Emerging Topics in Computational Intelligence, 3(3), pp 271-285
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Osman, OF, Elbashir, RMI, Abbass, IE, Kendrick, C, Goyal, M and Yap, MH (2017) Automated assessment of facial wrinkling: A case study on the effect of smoking. IEEE international conference on systems, man, and cybernetics (SMC), pp 1081-1086
Research team
Research team
Lead researcher
Co-researchers
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Kevin Walker, Image Metrics Ltd
PhD student
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Jireh Jam
Collaborating with:
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National Taiwan University of Science and Technology, Taiwan
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Sudan University of Science and Technology, Sudan
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Autonomous Transport Solutions Pre-development and Research, Sweden
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Panasonic R&D Center Singapore
Funding
With funding from
The Royal Society Industry Fellowship
Contact
Contact us
For general enquiries about this project and our Human-Centred Computing research theme, you can contact Dr Moi Hoon Yap.