深層学習手法による顔魅力要因の解釈可能性と産業応用への展望
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| Published in | 応用数理 Vol. 33; no. 2; pp. 89 - 93 |
|---|---|
| Main Author | |
| Format | Journal Article |
| Language | Japanese |
| Published |
一般社団法人 日本応用数理学会
23.06.2023
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| Online Access | Get full text |
| ISSN | 2432-1982 |
| DOI | 10.11540/bjsiam.33.2_89 |
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| Author | 佐野, 貴紀 |
|---|---|
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| Copyright | 2023 日本応用数理学会 |
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| References | [23] J. Xu, L. Jin, L. Liang, Z. Feng, and D. Xie. A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model. arXiv preprint, arXiv:1511.02465, 2015. [1] D. Gray, K. Yu, W. Xu, and Y. Gong. Predicting facial beauty without landmarks. In Computer Vision: ECCV 2010, pp. 434–447. Springer, 2010. [10] L. Liu, J. Xing, S. Liu, H. Xu, X. Zhou, and S. Yan. Wow! you are so beautiful today! ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 11, No. 1s, Article No. 20, 2014. [2] J. He, C. Wang, Y. Zhang, J. Guo, and Y. Guo. FA-GANs: Facial attractiveness enhancement with generative adversarial networks on frontal faces. arXiv preprint, arXiv:2005.08168, 2020. [8] L. Liang, L. Lin, L. Jin, D. Xie, and M. Li. Scut-fbp5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction. In 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1598–1603. IEEE, 2018. [6] L. Liang, L. Jin, and X. Li. Facial skin beautification using adaptive region-aware masks. IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2600–2612, 2014. [17] J. C. Peterson, S. Uddenberg, T. L. Griffiths, A. Todorov, and J. W. Suchow. Deep models of superficial face judgments. Proceedings of the National Academy of Sciences, Vol. 119, No. 17, e2115228119, 2022. [21] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradientbased localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision, pp. 618–626, 2017. [19] J. Saeed and A. M. Abdulazeez. Facial beauty prediction and analysis based on deep convolutional neural network: A review. Journal of Soft Computing and Data Mining, Vol. 2, No. 1, pp. 1–12, 2021. [11] X. Liu, T. Li, H. Peng, I. Chuoying Ouyang, T. Kim, and R. Wang. Understanding beauty via deep facial features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 246–256, 2019. [22] D. Xie, L. Liang, L. Jin, J. Xu, and M. Li. Scut-fbp: A benchmark dataset for facial beauty perception. In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1821–1826. IEEE, 2015. [15] D. I. Perrett, K. J. Lee, I. Penton-Voak, D. Rowland, S. Yoshikawa, D. M. Burt, S. P. Henzi, D. L. Castles, and S. Akamatsu. Effects of sexual dimorphism on facial attractiveness. Nature, Vol. 394, No. 6696, pp. 884–887, 1998. [14] T. V. Nguyen, S. Liu, B. Ni, J. Tan, Y. Rui, and S. Yan. Sense beauty via face, dressing, and/or voice. In Proceedings of the 20th ACM International Conference on Multimedia, pp. 239–248, 2012. [5] J. H. Langlois, L. Kalakanis, A. J. Rubenstein, A. Larson, M. Hallam, and M. Smoot. Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, Vol. 126, No. 3, pp. 390–423, 2000. [27] L. Zhang, D. Zhang, M.-M. Sun, and F.-M. Chen. Facial beauty analysis based on geometric feature: Toward attractiveness assessment application. Expert Systems with Applications, Vol. 82, pp. 252–265, 2017. [16] D. I. Perrett, K. A. May, and S. Yoshikawa. Facial shape and judgements of female attractiveness. Nature, Vol. 368, No. 6468, pp. 239–242, 1994. [26] Y. Zhai, Y. Huang, Y. Xu, J. Gan, H. Cao, W. Deng, R. D. Labati, V. Piuri, and F. Scotti. Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion. IEEE Access, Vol. 8, pp. 56892–56907, 2020. [12] N. Murray, L. Marchesotti, and F. Perronnin. Ava: A large-scale database for aesthetic visual analysis. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2408–2415. IEEE, 2012. [25] L. Xu, J. Xiang, and X. Yuan. Transferring rich deep features for facial beauty prediction. arXiv preprint, arXiv:1803.07253, 2018. [7] L. Liang, L. Jin, X. Zhang, and Y. Xu. Multiple facial image editing using edge–aware pde learning. Computer Graphics Forum, Vol. 34, pp. 203–212. 2015. [4] A. L. Jones, R. Russell, and R. Ward. Cosmetics alter biologically-based factors of beauty: Evidence from facial contrast. Evolutionary Psychology, Vol. 13, No. 1, 147470491501300113, 2015. [18] R. Russell. A sex difference in facial contrast and its exaggeration by cosmetics. Perception, Vol. 38, No. 8, pp. 1211–1219, 2009. [13] T. V. Nguyen and L. Liu. Smart mirror: Intelligent makeup recommendation and synthesis. In Proceedings of the 25th ACM International Conference on Multimedia, pp. 1253–1254, 2017. [3] V. S. Johnston. Mate choice decisions: the role of facial beauty. Trends in Cognitive Sciences, Vol. 10, No. 1, pp. 9–13, 2006. [20] T. Sano. Visualization of facial attractiveness factors using gradient-weighted class activation mapping to understand the connection between facial features and perception of attractiveness. International Journal of Affective Engineering, IJAE–D, 2022. [24] J. Xu, L. Jin, L. Liang, Z. Feng, D. Xie, and H. Mao. Facial attractiveness prediction using psychologically inspired convolutional neural network (picnn). In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1657–1661. IEEE, 2017. [9] L. Lin, L. Liang, and L. Jin. Regression guided by relative ranking using convolutional neural network (r3cnn) for facial beauty prediction. IEEE Transactions on Affective Computing, Vol. 13, No. 1, pp. 122–134, 2019. |
| References_xml | – reference: [15] D. I. Perrett, K. J. Lee, I. Penton-Voak, D. Rowland, S. Yoshikawa, D. M. Burt, S. P. Henzi, D. L. Castles, and S. Akamatsu. Effects of sexual dimorphism on facial attractiveness. Nature, Vol. 394, No. 6696, pp. 884–887, 1998. – reference: [22] D. Xie, L. Liang, L. Jin, J. Xu, and M. Li. Scut-fbp: A benchmark dataset for facial beauty perception. In 2015 IEEE International Conference on Systems, Man, and Cybernetics, pp. 1821–1826. IEEE, 2015. – reference: [9] L. Lin, L. Liang, and L. Jin. Regression guided by relative ranking using convolutional neural network (r3cnn) for facial beauty prediction. IEEE Transactions on Affective Computing, Vol. 13, No. 1, pp. 122–134, 2019. – reference: [11] X. Liu, T. Li, H. Peng, I. Chuoying Ouyang, T. Kim, and R. Wang. Understanding beauty via deep facial features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 246–256, 2019. – reference: [14] T. V. Nguyen, S. Liu, B. Ni, J. Tan, Y. Rui, and S. Yan. Sense beauty via face, dressing, and/or voice. In Proceedings of the 20th ACM International Conference on Multimedia, pp. 239–248, 2012. – reference: [19] J. Saeed and A. M. Abdulazeez. Facial beauty prediction and analysis based on deep convolutional neural network: A review. Journal of Soft Computing and Data Mining, Vol. 2, No. 1, pp. 1–12, 2021. – reference: [5] J. H. Langlois, L. Kalakanis, A. J. Rubenstein, A. Larson, M. Hallam, and M. Smoot. Maxims or myths of beauty? A meta-analytic and theoretical review. Psychological Bulletin, Vol. 126, No. 3, pp. 390–423, 2000. – reference: [4] A. L. Jones, R. Russell, and R. Ward. Cosmetics alter biologically-based factors of beauty: Evidence from facial contrast. Evolutionary Psychology, Vol. 13, No. 1, 147470491501300113, 2015. – reference: [6] L. Liang, L. Jin, and X. Li. Facial skin beautification using adaptive region-aware masks. IEEE Transactions on Cybernetics, Vol. 44, No. 12, pp. 2600–2612, 2014. – reference: [18] R. Russell. A sex difference in facial contrast and its exaggeration by cosmetics. Perception, Vol. 38, No. 8, pp. 1211–1219, 2009. – reference: [20] T. Sano. Visualization of facial attractiveness factors using gradient-weighted class activation mapping to understand the connection between facial features and perception of attractiveness. International Journal of Affective Engineering, IJAE–D, 2022. – reference: [3] V. S. Johnston. Mate choice decisions: the role of facial beauty. Trends in Cognitive Sciences, Vol. 10, No. 1, pp. 9–13, 2006. – reference: [2] J. He, C. Wang, Y. Zhang, J. Guo, and Y. Guo. FA-GANs: Facial attractiveness enhancement with generative adversarial networks on frontal faces. arXiv preprint, arXiv:2005.08168, 2020. – reference: [24] J. Xu, L. Jin, L. Liang, Z. Feng, D. Xie, and H. Mao. Facial attractiveness prediction using psychologically inspired convolutional neural network (picnn). In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1657–1661. IEEE, 2017. – reference: [7] L. Liang, L. Jin, X. Zhang, and Y. Xu. Multiple facial image editing using edge–aware pde learning. Computer Graphics Forum, Vol. 34, pp. 203–212. 2015. – reference: [10] L. Liu, J. Xing, S. Liu, H. Xu, X. Zhou, and S. Yan. Wow! you are so beautiful today! ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 11, No. 1s, Article No. 20, 2014. – reference: [16] D. I. Perrett, K. A. May, and S. Yoshikawa. Facial shape and judgements of female attractiveness. Nature, Vol. 368, No. 6468, pp. 239–242, 1994. – reference: [25] L. Xu, J. Xiang, and X. Yuan. Transferring rich deep features for facial beauty prediction. arXiv preprint, arXiv:1803.07253, 2018. – reference: [26] Y. Zhai, Y. Huang, Y. Xu, J. Gan, H. Cao, W. Deng, R. D. Labati, V. Piuri, and F. Scotti. Asian female facial beauty prediction using deep neural networks via transfer learning and multi-channel feature fusion. IEEE Access, Vol. 8, pp. 56892–56907, 2020. – reference: [8] L. Liang, L. Lin, L. Jin, D. Xie, and M. Li. Scut-fbp5500: A diverse benchmark dataset for multi-paradigm facial beauty prediction. In 2018 24th International Conference on Pattern Recognition (ICPR), pp. 1598–1603. IEEE, 2018. – reference: [23] J. Xu, L. Jin, L. Liang, Z. Feng, and D. Xie. A new humanlike facial attractiveness predictor with cascaded fine-tuning deep learning model. arXiv preprint, arXiv:1511.02465, 2015. – reference: [1] D. Gray, K. Yu, W. Xu, and Y. Gong. Predicting facial beauty without landmarks. In Computer Vision: ECCV 2010, pp. 434–447. Springer, 2010. – reference: [21] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra. Grad-cam: Visual explanations from deep networks via gradientbased localization. In Proceedings of the 2017 IEEE International Conference on Computer Vision, pp. 618–626, 2017. – reference: [12] N. Murray, L. Marchesotti, and F. Perronnin. Ava: A large-scale database for aesthetic visual analysis. In Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 2408–2415. IEEE, 2012. – reference: [27] L. Zhang, D. Zhang, M.-M. Sun, and F.-M. Chen. Facial beauty analysis based on geometric feature: Toward attractiveness assessment application. Expert Systems with Applications, Vol. 82, pp. 252–265, 2017. – reference: [13] T. V. Nguyen and L. Liu. Smart mirror: Intelligent makeup recommendation and synthesis. In Proceedings of the 25th ACM International Conference on Multimedia, pp. 1253–1254, 2017. – reference: [17] J. C. Peterson, S. Uddenberg, T. L. Griffiths, A. Todorov, and J. W. Suchow. Deep models of superficial face judgments. Proceedings of the National Academy of Sciences, Vol. 119, No. 17, e2115228119, 2022. |
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| Title | 深層学習手法による顔魅力要因の解釈可能性と産業応用への展望 |
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