Titre :

Facial Beauty Prediction Using an Ensemble of Deep Convolutional Neural Networks

Auteur(s) :

Boukhari, Djamel Eddine [Auteur]
Université d'El-Oued
Chemsa, Ali [Auteur]
Université d'El-Oued
Tahleb Ahmed, Abdelmalik [Auteur]
COMmunications NUMériques - IEMN [COMNUM - IEMN]
Ajgou, Riadh [Auteur]
Université d'El-Oued
Bouzaher, Mohamed Taher [Auteur]
Center for Scientific and Technical Research on Arid Regions [CRSTRA ]

Titre de la manifestation scientifique :

4th International Electronic Conference on Applied Sciences) (ASEC 2023)

Ville :

Online

Date de début de la manifestation scientifique :

2023-10-27

Titre de l’ouvrage :

Engineering Proceedings

Éditeur :

MDPI

Mot(s)-clé(s) en anglais :

deep learning
convolutional neural networks
facial beauty prediction
performance evaluation

Discipline(s) HAL :

Informatique [cs]/Vision par ordinateur et reconnaissance de formes [cs.CV]
Informatique [cs]/Apprentissage [cs.LG]

Résumé en anglais : [en]

The topic of facial beauty analysis has emerged as a crucial and fascinating subject of human culture. With various applications and significant attention from researchers, recent studies have investigated the relationship ...
Lire la suite >The topic of facial beauty analysis has emerged as a crucial and fascinating subject of human culture. With various applications and significant attention from researchers, recent studies have investigated the relationship between facial features and age, emotions, and other factors using multidisciplinary approaches. Facial beauty prediction is a significant visual recognition problem in the assessment of facial attractiveness, which is consistent with human perception. Overcoming the challenges associated with facial beauty prediction requires considerable effort due to the field’s novelty and lack of resources. In this vein, a deep learning method has recently demonstrated remarkable abilities in feature representation and analysis. Accordingly, this paper proposes an ensemble based on pre-trained convolutional neural network models to identify scores for facial beauty prediction. These ensembles are three separate deep convolutional neural networks, each with a unique structural representation built by previously trained models from Inceptionv3, Mobilenetv2, and a new simple network based on Convolutional Neural Networks (CNNs) for facial beauty prediction problems. According to the SCUT-FBP5500 benchmark dataset, the obtained 0.9350 Pearson coefficient experimental result demonstrated that using this ensemble of deep networks leads to a better prediction of facial beauty closer to human evaluation than conventional technology that spreads facial beauty. Finally, potential research directions are suggested for future research on facial beauty prediction.Lire moins >

Langue :

Anglais

Comité de lecture :

Oui

Audience :

Internationale

Vulgarisation :

Non

Commentaire :

The dataset SCUT-FBP5500 analyzed during the current study is available in the github repository, https://github.com/HCIILAB/SCUT-FBP5500-Database-Release (accessed on 15 June 2023).

Collections :

• Institut d'Électronique, de Microélectronique et de Nanotechnologie (IEMN) - UMR 8520

Source :

Harvested from HAL