CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Applied Intelligence Année : 2023

CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression

Résumé

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.
Fichier principal
Vignette du fichier
Applied Intelligence_Bougourzi_s10489-022-03943-0.pdf (2.34 Mo) Télécharger le fichier
Origine : Fichiers éditeurs autorisés sur une archive ouverte

Dates et versions

hal-03797287 , version 1 (04-10-2022)

Licence

Paternité

Identifiants

Citer

Fares Bougourzi, Fadi Dornaika, Nagore Barrena, Cosimo Distante, Abdelmalik Taleb-Ahmed. CNN based facial aesthetics analysis through dynamic robust losses and ensemble regression. Applied Intelligence, 2023, 53, pp.10825-10842. ⟨10.1007/s10489-022-03943-0⟩. ⟨hal-03797287⟩
19 Consultations
22 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More