Abstract
Charles Darwin once wrote: ‘It is certainly not true that there are in the mind of man any universal standards of beauty with respect to the human body’. The relation between facial beauty and the golden ratio is a known fact. In this paper, we have tried to establish the relation between face beauty and the golden ratio. Finally, we try to improve facial beauty using the golden ratio-based geometric transformation and some filtering operations. The work is divided into two parts: 1. verification of the relation between face beauty and golden ratio, 2. application of golden ratio for face beautification. The first part of the paper is based on the verification of a neoclassical theorem of beauty and the golden ratio based on the symmetry of the face, using various machine learning tools. Verification of the ratings is done using SCUT-FBP dataset. We used 450 images for the training purpose out of the 500 images and the rest 50 images are used for testing the data. The second part of the work is to beautify a face based on mathematical calculations and improve the skin texture, removes blemishes, and change the facial features according to the golden ratio. Test results show the significant improvement in facial beauty due to the application of the golden ratio.
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References
Otoole, A.J., Price, T., Vetter, T., Bartlett, J.C., Blanz, V.: 3D shape and 2D surface textures of human faces: the role of averages in attractiveness and age. Image Vis. Comput. 18, 9–19 (1999)
Atiyeh, B.S., Hayek, S.N.: Numeric expression of aesthetics and beauty. Aesthet. Plast. Surg. 32(2), 23–35 (2001)
Farkas, L.G.: Anthropometry of the Head and Face, 2nd edn. Raven Press, New York (1994)
Barnett, T.P., Preisendorfer, P.: Origins and levels of monthly and seasonal forecast skill for United States surface air temperatures determined by canonical correlation analysis. Mon, Weather Rev (1997)
Ho, T.K.: Random decision forests. In: Proceedings of the 3rd International Conference on Document Analysis and Recognition, pp. 278–282 (1995)
Ho, T.K.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Int. 20(8), 832–844 (1995)
Hastie, T., Tibshirani, R., Friedman, J.: The Elements of Statistical Learning, 2nd Ed. Springer (2008)
Farkas, L.G., Munro, I.R.: Anthropometric Facial Proportions in Medicine. Illinois (1987)
SCUT-FBP dataset. http://www.hcii-lab.net/data/SCUT-FBP/
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Roy, H., Dhar, S., Dey, K., Acharjee, S., Ghosh, D. (2018). An Automatic Face Attractiveness Improvement Using the Golden Ratio. In: Bhattacharyya, S., Chaki, N., Konar, D., Chakraborty, U., Singh, C. (eds) Advanced Computational and Communication Paradigms. Advances in Intelligent Systems and Computing, vol 706. Springer, Singapore. https://doi.org/10.1007/978-981-10-8237-5_73
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DOI: https://doi.org/10.1007/978-981-10-8237-5_73
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