Uncertainty-oriented Order Learning for Facial Beauty Prediction
This work improves facial beauty prediction for applications like cosmetic recommendations by addressing dataset and human inconsistencies, though it is incremental as it builds on order learning with uncertainty modeling.
The paper tackled the problem of facial beauty prediction by addressing inconsistencies in beauty standards across datasets and human cognition, proposing an uncertainty-oriented order learning method that outperformed state-of-the-art methods on accuracy and generalization across five datasets.
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.