Image-based face beauty analysis via graph-based semi-supervised learning

Automatic facial beauty analysis has become an emerging research topic. Despite some achieved advances, current methods and systems suffer from at least two limitations. Firstly, many developed systems rely on the use of ad-hoc hand-crafted features that were designed for generic pattern recognition problems. Secondly, while Deep Convolutional Neural Nets (DCNN) have been recently demonstrated to be a promising area of research in statistical machine learning, their use for automatic face beauty analysis may not guarantee optimal performances due to the use of a limited amount of face images with beauty scores. In this paper, we attempt to overcome these two main limitations by jointly exploiting two tricks. First, instead of using hand-crafted face features we use deep features of a pre-trained DCNN able to generate a high-level representation of a face image. Second, we exploit manifold learning theory and deploy three graph-based semi-supervised learning methods in order to enrich model learning without the need of additional labeled face images. These schemes perform graph-based score propagation. The proposed schemes were tested on three public datasets for beauty analysis: SCUT-FBP, (MB)-B-2, and SCUT-FBP5500. These experiments, as well as many comparisons with supervised schemes, show that the scheme coined Kernel Flexible Manifold Embedding compares favorably with many supervised schemes. They also show that its performances in terms of error prediction and Pearson Correlation are better than those reported for the used datasets.