# An Examination of Bias of Facial Analysis based BMI Prediction Models

arXiv: https://arxiv.org/abs/2204.10262 · HTML: https://mlsanity.com/paper/2204.10262/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 56 references that are in the map.
- First posted on arXiv 21 Apr 2022.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [CondenseNet: An Efficient DenseNet using Learned Group Convolutions](https://mlsanity.com/paper/1711.09224/) (2017)
- [FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age](https://mlsanity.com/paper/1908.04913/) (2019)
- [Characterizing the Variability in Face Recognition Accuracy Relative to Race](https://mlsanity.com/paper/1904.07325/) (2019)
- [How Does Gender Balance In Training Data Affect Face Recognition Accuracy?](https://mlsanity.com/paper/2002.02934/) (2020)
- [On Improving Cross-dataset Generalization of Deepfake Detectors](https://mlsanity.com/paper/2204.04285/) (2022)
- [Understanding Fairness of Gender Classification Algorithms Across Gender-Race Groups](https://mlsanity.com/paper/2009.11491/) (2020)
- [Anatomizing Bias in Facial Analysis](https://mlsanity.com/paper/2112.06522/) (2021)
- [On the Robustness of Face Recognition Algorithms Against Attacks and Bias](https://mlsanity.com/paper/2002.02942/) (2020)
- [An Experimental Evaluation on Deepfake Detection using Deep Face Recognition](https://mlsanity.com/paper/2110.01640/) (2021)
- [Face-to-BMI: Using Computer Vision to Infer Body Mass Index on Social Media](https://mlsanity.com/paper/1703.03156/) (2017)
- [Estimation of BMI from Facial Images using Semantic Segmentation based Region-Aware Pooling](https://mlsanity.com/paper/2104.04733/) (2021)
- [AI-based BMI Inference from Facial Images: An Application to Weight Monitoring](https://mlsanity.com/paper/2010.07442/) (2020)
- [Does Face Recognition Error Echo Gender Classification Error?](https://mlsanity.com/paper/2104.13803/) (2021)
- [Descriptors and regions of interest fusion for gender classification in the wild. Comparison and combination with Convolutional Neural Networks](https://mlsanity.com/paper/1507.06838/) (2015)

## Built on by (most cited first, 10 of 10)

- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups](https://mlsanity.com/paper/2208.08382/) (2022)
- [A Self-Supervised Learning Pipeline for Demographically Fair Facial Attribute Classification](https://mlsanity.com/paper/2407.10104/) (2024)
- [A Quantitative Evaluation of the Expressivity of BMI, Pose and Gender in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2503.06451/) (2025)
- [Is Facial Recognition Biased at Near-Infrared Spectrum As Well?](https://mlsanity.com/paper/2211.00129/) (2022)
- [MeFEm: Medical Face Embedding model](https://mlsanity.com/paper/2602.14672/) (2026)
- [PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction](https://mlsanity.com/paper/2311.18102/) (2023)
- [The Folly of AI for Age Verification](https://mlsanity.com/paper/2506.00038/) (2025)
- [Leveraging Text Guidance for Enhancing Demographic Fairness in Gender Classification](https://mlsanity.com/paper/2512.11015/) (2025)
- [AttriBE: Quantifying Attribute Expressivity in Body Embeddings for Recognition and Identification](https://mlsanity.com/paper/2604.27218/) (2026)

## Abstract (arXiv)

Obesity is one of the most important public health problems that the world is facing today. A recent trend is in the development of intervention tools that predict BMI using facial images for weight monitoring and management to combat obesity. Most of these studies used BMI annotated facial image datasets that mainly consisted of Caucasian subjects. Research on bias evaluation of face-based gender-, age-classification, and face recognition systems suggest that these technologies perform poorly for women, dark-skinned people, and older adults. The bias of facial analysis-based BMI prediction tools has not been studied until now. This paper evaluates the bias of facial-analysis-based BMI prediction models across Caucasian and African-American Males and Females. Experimental investigations on the gender, race, and BMI balanced version of the modified MORPH-II dataset suggested that the error rate in BMI prediction was least for Black Males and highest for White Females. Further, the psychology-related facial features correlated with weight suggested that as the BMI increases, the changes in the facial region are more prominent for Black Males and the least for White Females. This is the reason for the least error rate of the facial analysis-based BMI prediction tool for Black Males and highest for White Females.