Learning Disentangled Representations via Attribute Mixing for Improving Facial Beauty Prediction

Facial Beauty Prediction (FBP) aims to develop a machine that can automatically predict facial attractiveness. Recent advances demonstrate that deep learning models have achieved promising results in FBP tasks. However, conventional deep learning models lack the efficient ability to generalize to unseen attribute domain data, as attributes cause distribution discrepancy (asymmetry) among face data. To address this issue, we propose a simple yet effective method called MixAttr, a lightweight plug-and-play module that mixes two randomly selected feature statistics with different attributes to form a newly attributed feature. In this way, the feature space is implicitly enriched by increasing the diversity of features and mitigating the model shift caused by a single attribute, which benefits the decoupling of attributes and facial beauty prediction. Extensive experiments conducted to evaluate the properties and effectiveness of our method show that MixAttr can be flexibly inserted into existing network architectures to achieve state-of-the-art performance on different FBP benchmarks (e.g., a Pearson correlation of 0.9307 on SCUT-FBP5500). This feature mixing implicitly enriches the representation space, which is key to mitigating attribute-induced asymmetry and improving generalization. Additionally, we have also extended our method to the task of facial age estimation, demonstrating through superior experimental results that our method can also be applied to other attribute prediction tasks. We propose that distribution discrepancy in FBP can be viewed as a form of asymmetry in the feature space across different demographic groups. MixAttr mitigates this asymmetry by implicitly enriching the feature space and encouraging a more symmetric and attribute-invariant feature representation. By disentangling task-irrelevant attributes from task-oriented features, our method can improve both the accuracy and generalizability of deep models on tasks involving facial attribute prediction.