Regression Guided Strategy to Automated Facial Beauty Optimization through Image Synthesis

TL;DR

This paper introduces a data-driven method for facial beauty enhancement using GAN latent space optimization guided by a new regression network, outperforming rule-based approaches and enabling holistic aesthetic improvements.

Contribution

It proposes a novel approach combining GAN latent space manipulation with a facial beauty regression network, moving beyond rule-based methods for automatic aesthetic enhancement.

Findings

Regression network outperforms existing beauty evaluation models

Method effectively captures holistic beauty patterns from data

Enables dynamic facial beauty editing without predefined rules

Abstract

The use of beauty filters on social media, which enhance the appearance of individuals in images, is a well-researched area, with existing methods proving to be highly effective. Traditionally, such enhancements are performed using rule-based approaches that leverage domain knowledge of facial features associated with attractiveness, applying very specific transformations to maximize these attributes. In this work, we present an alternative approach that projects facial images as points on the latent space of a pre-trained GAN, which are then optimized to produce beautiful faces. The movement of the latent points is guided by a newly developed facial beauty evaluation regression network, which learns to distinguish attractive facial features, outperforming many existing facial beauty evaluation models in this domain. By using this data-driven approach, our method can automatically capture holistic patterns in beauty directly from data rather than relying on predefined rules, enabling more dynamic and potentially broader applications of facial beauty editing. This work demonstrates a potential new direction for automated aesthetic enhancement, offering a complementary alternative to existing methods.