# Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling

arXiv: https://arxiv.org/abs/2104.04733 · HTML: https://mlsanity.com/paper/2104.04733/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 42 references that are in the map.
- First posted on arXiv 10 Apr 2021.

## Builds on (most cited first, 9 of 9)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://mlsanity.com/paper/1604.02878/) (2016)
- [BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation](https://mlsanity.com/paper/1808.00897/) (2018)
- [MaskGAN: Towards Diverse and Interactive Facial Image Manipulation](https://mlsanity.com/paper/1907.11922/) (2019)
- [Human Semantic Parsing for Person Re-identification](https://mlsanity.com/paper/1804.00216/) (2018)
- [Improving Facial Attribute Prediction using Semantic Segmentation](https://mlsanity.com/paper/1704.08740/) (2017)
- [Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media](https://mlsanity.com/paper/1703.03156/) (2017)
- [AI-based BMI Inference from Facial Images: An Application to Weight Monitoring](https://mlsanity.com/paper/2010.07442/) (2020)

## Built on by (most cited first, 5 of 5)

- [An Examination of Bias of Facial Analysis based BMI Prediction Models](https://mlsanity.com/paper/2204.10262/) (2022)
- [Membership Inference Attacks Against Semantic Segmentation Models](https://mlsanity.com/paper/2212.01082/) (2022)
- [Human Health Indicator Prediction from Gait Video](https://mlsanity.com/paper/2212.12948/) (2022)
- [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)

Body-Mass-Index (BMI) conveys important information about one's life such as health and socio-economic conditions. Large-scale automatic estimation of BMIs can help predict several societal behaviors such as health, job opportunities, friendships, and popularity. The recent works have either employed hand-crafted geometrical face features or face-level deep convolutional neural network features for face to BMI prediction. The hand-crafted geometrical face feature lack generalizability and face-level deep features don't have detailed local information. Although useful, these methods missed the detailed local information which is essential for exact BMI prediction. In this paper, we propose to use deep features that are pooled from different face regions (eye, nose, eyebrow, lips, etc.,) and demonstrate that this explicit pooling from face regions can significantly boost the performance of BMI prediction. To address the problem of accurate and pixel-level face regions localization, we propose to use face semantic segmentation in our framework. Extensive experiments are performed using different Convolutional Neural Network (CNN) backbones including FaceNet and VGG-face on three publicly available datasets: VisualBMI, Bollywood and VIP attributes. Experimental results demonstrate that, as compared to the recent works, the proposed Reg-GAP gives a percentage improvement of 22.4% on VIP-attribute, 3.3% on VisualBMI, and 63.09% on the Bollywood dataset.