Facial reshaping operator for controllable face beautification

Hu, Shanfeng; Shum, Hubert P.H.; Liang, Xiaohui; Li, Frederick W.B.; Aslam, Nauman

Abstract

Posting attractive facial photos is part of everyday life in the social media era. Motivated by the demand, we propose a lightweight method to automatically and efficiently beautify the shapes of both portrait and non-portrait faces in photos, while allowing users to customize the beautification of individual facial features. Previous methods focus on the beautification of mostly frontal and neutral faces, without incorporating user controllability in the beautification process. To address these restrictions, we propose the Facial Reshaping Operator representation, which is affine-invariant, captures the pairwise geometric configuration of facial landmarks, and allows for efficient face beautification with the user-specified weights of individual facial parts. We also propose an unsupervised beautification method in the operator space of faces, where an input face is iteratively pulled towards a local nearby density mode with improved attractiveness. Our method distinguishes itself from the commercial beautification tools in that it mildly enhances facial shapes without altering makeups or complexions, which complements these tools that lack fine-grained control on the attractiveness of facial shapes for users. The experimental results show that our method improves facial shape attractiveness for a large range of poses and expressions, demonstrating the potential of applicability to photos seen on the social media such as Facebook and Instagram everyday.

Citation

Hu, S., Shum, H. P., Liang, X., Li, F. W., & Aslam, N. (2021). Facial reshaping operator for controllable face beautification. Expert Systems with Applications, 167, Article 114067. https://doi.org/10.1016/j.eswa.2020.114067

• Journal Article Type: Article
• Acceptance Date: Sep 27, 2020
• Online Publication Date: Oct 8, 2020
• Publication Date: Apr 1, 2021
• Deposit Date: Oct 30, 2020
• Publicly Available Date: Oct 8, 2021
• Journal: Expert Systems with Applications
• Print ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 167
• Article Number: 114067
• DOI: https://doi.org/10.1016/j.eswa.2020.114067
• Public URL: https://durham-repository.worktribe.com/output/1252290