Missing body measurements prediction in fashion industry: a comparative approach

Abstract

The use of artificial intelligence to predict body dimensions rather than measuring them by stylists or 3D scanners permits to obtain easily all measurements of individual consumers and can consequently reduce costs of population survey campaigns. In this paper, we have compared several models of machine learning to predict about 30 measurements used in fashion industry to construct clothes from 6 easy-to-measure body dimensions and demographic information. The four types of models we have studied are linear regressions, random forests, gradient boosting trees and support vector regressions. To construct and train them we have used anthropometric measurements of 9000 adult individuals of the French population collected by the French Institute of Textiles and Clothing (IFTH) during a national measurement campaign collected between 2003 and 2005. We have analyzed the model prediction performance in terms of individual and global predictions as well as the effect of the training dataset size and the importance of the input features. The linear and the support vector regressions have given the best results with respect to evaluation metrics, predicted distributions and have required less training data than tree-based models. It turns out that the weight and height have been the most important input features for the models considered while the hip girth has been the less important among the input measurements. Since the set of body dimensions used in fashion industry and the morphology depend on the gender, we have decided to treat men and women separately and to compare them.