BMI Prediction Using Facial Features with Deep Learning Techniques

Body Mass Index (BMI) is a simple measure that links a person’s weight to their height. It’s widely used to assess health levels and risks. But measuring height and weight can be inconvenient. Or not always possible. In this study, we explore an alternative—predicting BMI from facial images. One such approach involves leveraging facial features extracted from images to predict BMI, eliminating the need for direct physical measurements. This study presents a deep learning-based system that utilizes Convolutional Neural Networks (CNNs), particularly ResNet50 architecture, to analyze facial images and predict BMI through a regression layer. The model is trained on pre-processed facial datasets, using Haar cascade classifiers for face detection and standardization. Once the features are extracted, the system classifies the estimated BMI into standard health categories such as underweight, normal, overweight, or obese. This approach offers a non-intrusive, practical alternative for BMI estimation, particularly useful in healthcare applications, mobile health platforms, and wellness tools. Using Convolutional Neural Networks (CNNs) and regression models, we created a system that takes in a face and gives an estimated BMI. No physical measurements. Just one photo. This approach can help in healthcare, social platforms, or any scenario where quick BMI estimation is useful.