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.
Data files:
  1. [1] D. Zhang, F. Chen, and Y. Xu, “Computer Models for Facial Beauty Analysis,” Springer, 2016.
  2. [2] J. Fan et al., “Prediction of facial attractiveness from facial proportions,” Pattern Recognition, Vol.45, No.6, pp. 2326-2334, 2012.
  3. [3] H. Knight and O. Keith, “Ranking facial attractiveness,” European J. of Orthodontics, Vol.27, No.4, pp. 340-348, 2005.
  4. [4] H. Doho, H. Nishimura, and S. Nobukawa, “Dynamic pattern recognition model based on neural network response to signal fluctuation,” J. Adv. Comput. Intell. Intell. Inform., Vol.27, No.1, pp. 44-53, 2023.
  5. [5] K. Cao et al., “Deep learning for facial beauty prediction,” Information, Vol.11, No.8, Article No.391, 2020.
  6. [6] D. Kanda, S. Kawai, and H. Nobuhara, “Visualization method corresponding to regression problems and its application to deep learning-based gaze estimation model,” J. Adv. Comput. Intell. Intell. Inform., Vol.24, No.5, pp. 676-684, 2020.
  7. [7] J. N. Saeed and A. M. Abdulazeez, “Facial beauty prediction and analysis based on deep convolutional neural network: A review,” J. of Soft Computing and Data Mining, Vol.2, No.1, pp. 1-12, 2021.
  8. [8] D. Gray et al., “Predicting facial beauty without landmarks,” Proc. of the 11th European Conf. on Computer Vision (ECCV 2010), Part VI, pp. 434-447, 2010.
  9. [9] D. Xie et al., “SCUT-FBP: A benchmark dataset for facial beauty perception,” 2015 IEEE Int. Conf. on Systems, Man, and Cybernetics, pp. 1821-1826, 2015.
  10. [10] J. Gan et al., “2M BeautyNet: Facial beauty prediction based on multi-task transfer learning,” IEEE Access, Vol.8, pp. 20245-20256, 2020.
  11. [11] F. Dornaika et al., “Efficient deep discriminant embedding: Application to face beauty prediction and classification,” Engineering Applications of Artificial Intelligence, Vol.95, Article No.103831, 2020.
  12. [12] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Proc. of the 25th Int. Conf. on Neural Information Processing Systems (NIPS’12), Vol.1, pp. 1097-1105, 2012.
  13. [13] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” arXiv: 1409.1556, 2014.
  14. [14] C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015.
  15. [15] [Accessed January 12, 2023]
  16. [16] J. Gan et al., “Facial beauty prediction based on lighted deep convolution neural network with feature extraction strengthened,” Chinese J. of Electronics, Vol.29, No.2, pp. 312-321, 2020.
  17. [17] S. Peng et al., “More trainable inception-ResNet for face recognition,” Neurocomputing, Vol.411, pp. 9-19, 2020.
  18. [18] H. Zhang et al., “ResNeSt: Split-attention networks,” 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW), 2022.
  19. [19] M. Tan and Q. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” Proc. of the 36th Int. Conf. on Machine Learning (PMLR), pp. 6105-6114, 2019.
  20. [20] F. Bougourzi, F. Dornaika, and A. Taleb-Ahmed, “Deep learning based face beauty prediction via dynamic robust losses and ensemble regression,” Knowledge-Based Systems, Vol.242, Article No.108246, 2022.
  21. [21] B. Wu et al., “FBNet: Hardware-aware efficient ConvNet design via differentiable neural architecture search,” 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 10726-10734, 2019.
  22. [22] M. Tan et al., “MnasNet: Platform-aware neural architecture search for mobile,” 2019 IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2815-2823, 2019.
  23. [23] T. Mingxing and Q. V. Le, “MixConv: Mixed depthwise convolutional kernels,” arXiv: 1907.09595, 2019.
  24. [24] M. Sandler et al., “MobileNetV2: Inverted residuals and linear bottlenecks,” 2018 IEEE/CVF Conf. on Computer Vision and Pattern Recognition, pp. 4510-4520, 2018.
  25. [25] S. An et al., “An ensemble of simple convolutional neural network models for MNIST digit recognition,” arXiv: 2008.10400, 2020.
  26. [26] L. Liang et al., “SCUT-FBP5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction,” 2018 24th Int. Conf. on Pattern Recognition (ICPR), pp. 1598-1603, 2018.
  27. [27] L. Lin, L. Liang, and L. Jin, “Regression guided by relative ranking using convolutional neural network (R3CNN) for facial beauty prediction,” IEEE Trans. on Affective Computing, Vol.13, No.1, pp. 122-134, 2019.
  28. [28] D. Albashish, “Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images,” PeerJ Computer Science, Vol.8, Article No.e1031, 2022.
  29. [29] G. Huang et al., “Densely connected convolutional networks,” 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017.
  30. [30] E. Vahdati and C. Y. Suen, “Facial beauty prediction using transfer and multi-task learning techniques,” Proc. of the 2nd Int. Conf. on Pattern Recognition and Artificial Intelligence (ICPRAI 2020), pp. 441-452, 2020.
  31. [31] C. Szegedy et al., “Rethinking the inception architecture for computer vision,” 2016 IEEE Conf. on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
  32. [32] L. Alzubaidi et al., “Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions,” J. of Big Data, Vol.8, No.1, Article No.53, 2021.
  33. [33] B. Koonce, “EfficientNet,” B. Koonce, “Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization,” pp. 109-123, Apress, 2021.
  34. [34] S. Shi et al., “Improving facial attractiveness prediction via co-attention learning,” 2019 IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), pp. 4045-4049, 2019.
  35. [35] F. Dornaika and A. Moujahid, “Multi-view graph fusion for semi-supervised learning: Application to image-based face beauty prediction,” Algorithms, Vol.15, No.6, Article No.207, 2022.
  36. [36] I. Lebedeva, Y. Guo, and F. Ying, “Transfer learning adaptive facial attractiveness assessment,” J. of Physics: Conf. Series, Vol.1922, Article No.012004, 2021.
  37. [37] I. Lebedeva, F. Ying, and Y. Guo, “Personalized facial beauty assessment: A meta-learning approach,” The Visual Computer, Vol.39, No.3, pp. 1095-1107, 2023.
  38. [38] F. Chen and D. Zhang, “A benchmark for geometric facial beauty study,” Proc. of the 2nd Int. Conf. on Medical Biometrics (ICMB 2010), pp. 21-32, 2010.
  39. [39] D. T. Long, “A facial expressions recognition method using residual network architecture for online learning evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.25, No.6, pp. 953-962, 2021.
  40. [40] P. Zhang and Y. Liu, “NAS4FBP: Facial beauty prediction based on neural architecture search,” Proc. of the 31st Int. Conf. on Artificial Neural Networks (ICANN 2022), pp. 225-236, 2022.

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Last updated on Nov. 24, 2023