A novel method to estimate Height, Weight and Body Mass Index from face images

Body Mass Index (BMI) is the most commonly used tool to evaluate an individual's health. It is used to classify a person as underweight, healthy weight, overweight or obese. BMI is co-related with body fat and is a vital indicator of possible diseases that can transpire with higher body fat ranges. Higher body fat is prevalent these days with a higher calorie diet and a low physical activity lifestyle. On the other end of the spectrum, Adult malnutrition is more common and widespread than we are conscious of these days. The BMI can be used as a measure of adult nutritional status, both of individuals and of communities. Given that people have less time in their busy life and most people dont own a weighing machine and/or a measuring tape, we propose a time and cost efficient method of estimating Height, Weight and BMI from a persons face. In this paper, we propose a novel model using Convolution Neural Networks (CNN) and Artificial Neural Networks (ANN). We start by detecting the face from an image using the Viola-Jones algorithm. The image is fed to the Feature Extractor model. The extracted features are passed to an Artificial Neural Network (ANN) model which gives the predicted Height, Weight and BMI values. We have evaluated our model on the Reddit-HWBMI dataset and Face-to-BMI dataset. We propose a novel dataset, the Reddit-HWBMI dataset which contains 982 subjects with their corresponding Height, Weight, BMI, Gender and Age. The best performance for BMI was given by the XceptionNet model when used as a Feature Extractor. The XceptionNet also performed best for weight, whereas VGG-Face (Resnet model) performed slightly better than XceptionNet for height.