Deep learning based face beauty prediction via dynamic robust losses and ensemble regression

In the last decade, several studies have shown that facial attractiveness can be learned by machines. In this paper, we address Facial Beauty Prediction from static images. The paper contains three main contributions. First, we propose a two-branch architecture (REX-INCEP) based on merging the architecture of two already trained networks to deal with the complicated high-level features associated with the FBP problem. Second, we introduce the use of a dynamic law to control the behaviour of the following robust loss functions during training: ParamSmoothL1, Huber and Tukey. Third, we propose an ensemble regression based on Convolutional Neural Networks (CNNs). In this ensemble, we use both the basic networks and our proposed network (REX-INCEP). The proposed individual CNN regressors are trained with different loss functions, namely MSE, dynamic ParamSmoothL1, dynamic Huber and dynamic Tukey. Our approach is evaluated on the SCUT-FBP5500 database using the two evaluation scenarios provided by the database creators: 60%-40% split and five-fold cross-validation. In both evaluation scenarios, our approach outperforms the state of the art on several metrics. These comparisons highlight the effectiveness of the proposed solutions for FBP. They also show that the proposed dynamic robust losses lead to more flexible and accurate estimators. (c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).