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

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

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.

来自面部图像的基于AI的BMI推断：在体重监测中的应用

随着肥胖的惊人趋势，基于自我诊断图像的健康体重监测方法越来越引起人们的关注。仅有少数学术研究调查了基于AI的从面部图像推断体重指数（BMI）的方法，以作为健康体重监测和管理的解决方案。.. 为了促进该领域的进一步研究和开发，我们评估和比较了五种基于深度学习的卷积神经网络（CNN）架构的性能，即VGG19，ResNet50，DenseNet，MobileNet和lightCNN用于从面部图像进行BMI推理。在从社交媒体组装的三个可公开获得BMI注释的面部图像数据集（VisualBMI，VIP属性和宝莱坞数据集）上的实验结果表明，深度学习方法在从具有最小平均绝对误差（MAE）的面部图像进行BMI推理中的功效的） 1.04 使用ResNet50获得。 （阅读更多）