MetaFBP: Learning to Learn High-Order Predictor for Personalized Facial
  Beauty Prediction
Luojun Lin Zhifeng Shen Jia-Li Yin Qipeng Liu Yuanlong Yu Weijie Chen
Predicting individual aesthetic preferences holds significant practical
applications and academic implications for human society. However, existing
studies mainly focus on learning and predicting the commonality of facial
attractiveness, with little attention given to Personalized Facial Beauty
Prediction (PFBP). PFBP aims to develop a machine that can adapt to individual
aesthetic preferences with only a few images rated by each user. In this paper,
we formulate this task from a meta-learning perspective that each user
corresponds to a meta-task. To address such PFBP task, we draw inspiration from
the human aesthetic mechanism that visual aesthetics in society follows a
Gaussian distribution, which motivates us to disentangle user preferences into
a commonality and an individuality part. To this end, we propose a novel
MetaFBP framework, in which we devise a universal feature extractor to capture
the aesthetic commonality and then optimize to adapt the aesthetic
individuality by shifting the decision boundary of the predictor via a
meta-learning mechanism. Unlike conventional meta-learning methods that may
struggle with slow adaptation or overfitting to tiny support sets, we propose a
novel approach that optimizes a high-order predictor for fast adaptation. In
order to validate the performance of the proposed method, we build several PFBP
benchmarks by using existing facial beauty prediction datasets rated by
numerous users. Extensive experiments on these benchmarks demonstrate the
effectiveness of the proposed MetaFBP method.