Toward graph-based semi-supervised face beauty prediction

Assessing beauty using facial images analysis is an emerging computer vision problem. To the best of our knowledge, all existing methods for automatic facial beauty scoring rely on fully supervised schemes. In this paper, we introduce the use of semi-supervised learning schemes for solving the problem of face beauty scoring when the image descriptor is holistic and the score is given by a real number. The paper has two main contributions. Firstly, we introduce the use of graph-based semi-supervised learning for face beauty scoring. The proposed method is based on texture and utilizes continuous scores in a full range. Secondly, we adapt and kernelize an existing linear Flexible Manifold Embedding scheme (that works with discrete classes) to the case of real scores propagation. The resulting model can be used for transductive and inductive settings. The proposed semi-supervised schemes were evaluated on three recent public datasets for face beauty analysis: SCUT-FBP, M2B, and SCUT-FBP5500. The obtained experimental results, as well as many comparisons with fully supervised methods, demonstrate that the nonlinear semi-supervised scheme compares favorably with many supervised schemes. The proposed semi-supervised scoring framework paves the way to virtually all applications to adopt continuous scores instead of the usual discrete labels.