Nonlinear, flexible, semisupervised learning scheme for face beauty scoring

Automatic facial beauty scoring in images is an emerging research topic in face-based biometrics. All existing methods adopt fully supervised schemes. We introduce the use of semisupervised learning schemes for solving the problem of face beauty scoring. The paper has two main contributions. First, instead of using fully supervised techniques, we show that graph-based score propagation methods can enrich model learning without the need of additional labeled face images. Second, we propose a nonlinear flexible manifold embedding for solving the score propagation. This model can be used for transductive and inductive settings. The proposed semisupervised schemes were tested on three recent public datasets for face beauty analysis: SCUT-FBP, M2B, and SCUT-FBP5500. These experiments, as well as many comparisons with supervised schemes, show that the nonlinear semisupervised scheme 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.