Laburpena
In recent years, estimating beauty of faces has attracted growing interest in the fields of computer vision and machine
learning. This is due to the emergence of face beauty datasets (such as SCUT-FBP, SCUT-FBP5500 and KDEF-PT) and
the prevalence of deep learning methods in many tasks. The goal of this work is to leverage the advances in Deep
Learning architectures to provide stable and accurate face beauty estimation from static face images. To this end, our
proposed approach has three main contributions. To deal with the complicated high-level features associated with the FBP
problem by using more than one pre-trained Convolutional Neural Network (CNN) model, we propose an architecture with
two backbones (2B-IncRex). In addition to 2B-IncRex, we introduce a parabolic dynamic law to control the behavior
of the robust loss parameters during training. These robust losses are ParamSmoothL1, Huber, and Tukey. As a third
contribution, we propose an ensemble regression based on five regressors, namely Resnext-50, Inception-v3 and three
regressors based on our proposed 2B-IncRex architecture. These models are trained with the following dynamic loss
functions: Dynamic ParamSmoothL1, Dynamic Tukey, Dynamic ParamSmoothL1, Dynamic Huber, and Dynamic Tukey,
respectively. To evaluate the performance of our approach, we used two datasets: SCUT-FBP5500 and KDEF-PT. The
dataset SCUT-FBP5500 contains two evaluation scenarios provided by the database developers: 60-40% split and five-
fold cross-validation. Our approach outperforms state-of-the-art methods on several metrics in both evaluation scenarios of
SCUT-FBP5500. Moreover, experiments on the KDEF-PT dataset demonstrate the efficiency of our approach for estimating
facial beauty using transfer learning, despite the presence of facial expressions and limited data. 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.