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

• URL: http://arxiv.org/abs/2010.07442v1
• Date: Thu, 15 Oct 2020 00:00:40 GMT
• Title: AI-based BMI Inference from Facial Images: An Application to Weight Monitoring
• Authors: Hera Siddiqui, Ajita Rattani, Dakshina Ranjan Kisku, Tanner Dean
• Abstract summary: We evaluate and compare the performance of five different CNN architectures for BMI inference from facial images. Experimental results 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.
• Score: 3.4601380631551146
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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