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

arXiv: https://arxiv.org/abs/2010.07442 · HTML: https://mlsanity.com/paper/2010.07442/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [An Examination of Bias of Facial Analysis based BMI Prediction Models](https://mlsanity.com/paper/2204.10262/) (2022)
- [Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling](https://mlsanity.com/paper/2104.04733/) (2021)
- [PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction](https://mlsanity.com/paper/2311.18102/) (2023)
- [BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network](https://mlsanity.com/paper/2409.02584/) (2024)

## Abstract (arXiv)

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.