[go: up one dir, main page]
Top > JACIII > An Ensemble of Deep Convolutional Neural Networks Models for F ...

single-jc.php

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

Research Paper:

Views over last 60 days: 337

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

Received:
February 14, 2023
Accepted:
August 16, 2023
Published:
November 20, 2023
Creative Commons License
Keywords:
convolutional neural networks, facial beauty prediction, deep learning, performance evaluation
Abstract

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:
References
  1. [1] D. Zhang, F. Chen, and Y. Xu, “Computer Models for Facial Beauty Analysis,” Springer, 2016. https://doi.org/10.1007/978-3-319-32598-9
  2. [2] J. Fan et al., “Prediction of facial attractiveness from facial proportions,” Pattern Recognition, Vol.45, No.6, pp. 2326-2334, 2012. https://doi.org/10.1016/j.patcog.2011.11.024
  3. [3] H. Knight and O. Keith, “Ranking facial attractiveness,” European J. of Orthodontics, Vol.27, No.4, pp. 340-348, 2005. https://doi.org/10.1093/ejo/cji042
  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. https://doi.org/10.20965/jaciii.2023.p0044
  5. [5] K. Cao et al., “Deep learning for facial beauty prediction,” Information, Vol.11, No.8, Article No.391, 2020. https://doi.org/10.3390/info11080391
  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. https://doi.org/10.20965/jaciii.2020.p0676
  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. https://doi.org/10.30880/jscdm.2021.02.01.001
  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. https://doi.org/10.1007/978-3-642-15567-3_32
  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. https://doi.org/10.1109/SMC.2015.319
  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. https://doi.org/10.1109/ACCESS.2020.2968837
  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. https://doi.org/10.1016/j.engappai.2020.103831
  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. https://doi.org/10.48550/arXiv.1409.1556
  14. [14] C. Szegedy et al., “Going deeper with convolutions,” 2015 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2015. https://doi.org/10.1109/CVPR.2015.7298594
  15. [15] https://github.com/DjameleddineBoukhari/ENCNN [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. https://doi.org/10.1049/cje.2020.01.009
  17. [17] S. Peng et al., “More trainable inception-ResNet for face recognition,” Neurocomputing, Vol.411, pp. 9-19, 2020. https://doi.org/10.1016/j.neucom.2020.05.022
  18. [18] H. Zhang et al., “ResNeSt: Split-attention networks,” 2022 IEEE/CVF Conf. on Computer Vision and Pattern Recognition Workshop (CVPRW), 2022. https://doi.org/10.1109/CVPRW56347.2022.00309
  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. https://doi.org/10.1016/j.knosys.2022.108246
  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. https://doi.org/10.1109/CVPR.2019.01099
  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. https://doi.org/10.1109/CVPR.2019.00293
  23. [23] T. Mingxing and Q. V. Le, “MixConv: Mixed depthwise convolutional kernels,” arXiv: 1907.09595, 2019. https://doi.org/10.48550/arXiv.1907.09595
  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. https://doi.org/10.1109/CVPR.2018.00474
  25. [25] S. An et al., “An ensemble of simple convolutional neural network models for MNIST digit recognition,” arXiv: 2008.10400, 2020. https://doi.org/10.48550/arXiv.2008.10400
  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. https://doi.org/10.1109/ICPR.2018.8546038
  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. https://doi.org/10.1109/TAFFC.2019.2933523
  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. https://doi.org/10.7717/peerj-cs.1031
  29. [29] G. Huang et al., “Densely connected convolutional networks,” 2017 IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pp. 2261-2269, 2017. https://doi.org/10.1109/CVPR.2017.243
  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. https://doi.org/10.1007/978-3-030-59830-3_38
  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. https://doi.org/10.1109/CVPR.2016.308
  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. https://doi.org/10.1186/s40537-021-00444-8
  33. [33] B. Koonce, “EfficientNet,” B. Koonce, “Convolutional Neural Networks with Swift for Tensorflow: Image Recognition and Dataset Categorization,” pp. 109-123, Apress, 2021. https://doi.org/10.1007/978-1-4842-6168-2_10
  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. https://doi.org/10.1109/ICASSP.2019.8683112
  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. https://doi.org/10.3390/a15060207
  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. https://doi.org/10.1088/1742-6596/1922/1/012004
  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. https://doi.org/10.1007/s00371-021-02387-w
  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. https://doi.org/10.1007/978-3-642-13923-9_3
  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. https://doi.org/10.20965/jaciii.2021.p0953
  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. https://doi.org/10.1007/978-3-031-15934-3_19

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 04, 2024