Document Type : Full Research Paper
Authors
1 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran
2 Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
Abstract
Currently, the rapid growth of the beauty industry, along with the development of intelligent models based on machine learning algorithms, has led to an increase in extensive research in this field. Rhinoplasty is one of the most common and demanding facial cosmetic surgeries because the nose is the most prominence element of the face, which has a great impact on its attractiveness. The purpose of this article is to present a machine learning-based framework for predicting nasal aesthetic evaluation. In this article, a set of geometric parameters of the nose relative to the entire face are given as input and human rating as output to the popular machine learning regression algorithms. An ablation study was then carried out to examine the influence of facial shape, skin color, and texture on the beauty of the nose. Multilayer perceptron classification, K-means clustering, and grey level co-occurrence matrix were used to extract facial shape, skin color, and texture. The results show that the model based on geometric parameters has a moderate correlation with human rating, and by adding each subset of the features of face shape, color, and skin texture, the correlation of the obtained model increases until a high degree of correlation is achieved. The results also show that the random forest algorithm has the best performance among other algorithms based on the evaluation criteria of absolute mean error, root mean square error, and Pearson correlation. The results of this study show that the proposed framework can be helpful in determining the beauty of the nose.
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