AI-based BMI Inference from Facial Images: An Application to Weight Monitoring
This work addresses weight monitoring for health applications, but it is incremental as it applies existing methods to a specific domain.
The paper tackled the problem of inferring Body Mass Index (BMI) from facial images for weight monitoring by evaluating five deep-learning CNN architectures, achieving a minimum mean absolute error of 1.04 using ResNet50.
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.