Uncertainty-oriented Order Learning for Facial Beauty Prediction

TL;DR

This paper introduces Uncertainty-oriented Order Learning (UOL) for facial beauty prediction, addressing dataset and human cognition inconsistencies by learning facial beauty order relations and modeling uncertainty, leading to improved accuracy and generalization.

Contribution

The paper proposes a novel UOL framework that models order relations and uncertainty in facial beauty prediction, enhancing robustness across datasets.

Findings

UOL outperforms state-of-the-art methods in accuracy.

UOL demonstrates superior generalization ability.

Extensive experiments validate the effectiveness of UOL.

Abstract

Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability.