AI-based BMI Inference from Facial Images: An Application to Weight Monitoring

TL;DR

This paper evaluates five deep learning CNN architectures for BMI inference from facial images, demonstrating promising results with minimal error, to support healthy weight monitoring and obesity management.

Contribution

It compares the performance of five CNN models on publicly available facial image datasets for BMI prediction, highlighting the effectiveness of deep learning in this application.

Findings

ResNet50 achieved the lowest MAE of 1.04.

Deep learning models show potential for accurate BMI inference from face images.

Evaluation on three datasets confirms the viability of AI-based weight monitoring.

Abstract

Self-diagnostic image-based methods for healthy weight monitoring is gaining increased interest following the alarming trend of obesity. Only a handful of academic studies exist that investigate AI-based methods for Body Mass Index (BMI) inference from facial images as a solution to healthy weight monitoring and management. To promote further research and development in this area, we evaluate and compare the performance of five different deep-learning based Convolutional Neural Network (CNN) architectures i.e., VGG19, ResNet50, DenseNet, MobileNet, and lightCNN for BMI inference from facial images. Experimental results on the three publicly available BMI annotated facial image datasets assembled from social media, namely, VisualBMI, VIP-Attributes, and Bollywood datasets, suggest the efficacy of the deep learning methods in BMI inference from face images with minimum Mean Absolute Error (MAE) of $1.04$ obtained using ResNet50.