Al-based BMI inference from facial images: An application to weight monitoring

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Authors
Siddiqui, Hera
Rattani, Ajita
Kisku, Dakshina Ranjan
Dean, Tanner
Issue Date
2021-02-23
Type
Conference paper
Language
en_US
Keywords
Obesity , Social networking (online) , Neural networks , Market research , Indexes , Monitoring , Research and development
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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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Siddiqui, H., Rattani, A., Kisku, D. R., & Dean, T. (2020). Al-based BMI inference from facial images: An application to weight monitoring. Paper presented at the Proceedings - 19th IEEE International Conference on Machine Learning and Applications, ICMLA 2020, 1101-1105. doi:10.1109/ICMLA51294.2020.00177
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IEEE
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