JACIII Vol.27 No.6 pp. 1209-1215
doi: 10.20965/jaciii.2023.p1209

Research Paper:

An Ensemble of Deep Convolutional Neural Networks Models for Facial Beauty Prediction

Djamel Eddine Boukhari*,† ORCID Icon, Ali Chemsa* ORCID Icon, Riadh Ajgou* ORCID Icon, and Mohamed Taher Bouzaher** ORCID Icon

*Laboratoire de Génie Electrique et des Energies Renouvelables d’El Oued, Department of Electrical Engineering, University of El Oued
El Oued, El Oued 39000, Algeria

Corresponding author

**Scientific and Technical Research Centre on Arid Regions (CRSTRA)
Biskra, Algeria

February 14, 2023
August 16, 2023
November 20, 2023
convolutional neural networks, facial beauty prediction, deep learning, performance evaluation

Facial beauty prediction is an emerging topic. The pursuit of facial beauty is the nature of human beings. As the demand for aesthetic surgery has increased significantly over the past few years, an understanding beauty is becoming increasingly important in medical settings. This work proposes a new ensemble based on the pre-trained convolutional neural network (CNN) models to identify scores for facial beauty prediction. These ensembles were originally built from the following previously trained models: DenseNet-201, Inception-v3, MobileNetV2, and EfficientNetB7. According to the SCUT-FBP5500 benchmark dataset, the proposed model obtains a Pearson coefficient of 0.9469. This reveals that the suggested EN-CNNs model can be successfully applied in a variety of face-to-face applications.

The proposed ensemble of deep CNN

The proposed ensemble of deep CNN

Cite this article as:
D. Boukhari, A. Chemsa, R. Ajgou, and M. Bouzaher, “An Ensemble of Deep Convolutional Neural Networks Models for Facial Beauty Prediction,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1209-1215, 2023.
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Last updated on Jul. 19, 2024