Prediction of Bust and Waist Size Based on Two-Dimensional Images (Journal of Fiber Bioengineering and Informatics, 2024)
Human body measurement based on two-dimensional images has been widely applied in the clothingindustry due to its cost and operational advantages. However, the current accuracy of human bodycircumference measurement is low. This article aims to propose a high-precision method for measuringhuman body circumference, taking bust circumference and waist circumference as examples, and based on120 virtual simulations of human bodies, proposes a method to extract human body bust circumferencesize from front and side angles images. Using the feature value pixel size to calculate the trapezoidperimeter and the ellipse perimeter, and comparing them with the difference of bust circumference andwaist circumference sizes, machine learning is applied to build a size prediction model, thus obtainingthe values of bust circumference and waist circumference. The experimental results show that theaverage prediction errors of bust girth and waist girth by the proposed method are 0.26 cm and 0.24cm, respectively, indicating good prediction performance and applicability for practical production. Theproposed method effectively reduces the measurement errors of girth dimensions in image measurementand provides methods and ideas for non-contact human body measurement research.
Keywords: Anthropometric dimension; Girth measurement; Neural network; Linear regression.