Face to BMI: A Deep Learning Based Approach for Computing BMI from Face
Abstract
Body mass index (BMI) is a measure of a
person's health in relation to their body weight. BMI
has been shown to correlate with various factors such
as physical health, mental health, and prevalence.
Calculating BMI often requires exact height and
weight, which will require manual work to measure.
Large scale automation of BMI calculation can be used
to analyze different aspects of society and can be used
by governments and businesses for to make effective
decisions. Previous work used only geometric facial
features that removed other information or the datadriven
deep learning approach where the amount of
data became a bottleneck. We used pre-trained modern
models such as Inception-v3, VGG-Faces, VGG19,
Xception and refined them on a relatively large public
dataset with discriminant learning. We used the largest
dataset of faces labeled Illinois DOC for training and
Capturing Profile, VIP attribute for evaluation
purposes.
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