Predicting body measures from 2D images using Convolutional Neural Networks

Nutrition is a significant determinant of health, the resolution of many nutritional issues, initially requires an anthropometry examination. Body measures provide data for studying the relationship between diet, nutritional status, and health. Manual and automatic methods can perform body measurements. The manual method usually uses an anthropometric tape. However, the automatic process uses the equipment of Dual-energy X-ray absorptiometry (DXA). Our work presents a new approach to calculate body measures using 2D Camera Images, applying Digital Image Processing, Convolution Neural Networks, and Machine Learning techniques. The dataset used contains 38 exams, for each exam, has four digital images and the dimensions of body measurements, performed by a specialist. The methods used in this work for segmentation were Dense Human Pose Estimation - CNN with the Bayesian, K-Nearest Neighbors, Support Vector Machine, Decision Threes, Adaptive Boosting, Random Forest, Multilayer Perceptron and Expectation-Maximization classifiers. The approach with Dense Human Pose Estimation and Expectation-Maximization reached the best results, with mean squared error (MSE) always bellow 4.606 ± 3.412 cm when compared with specialist measures.