论文标题
来自面部图像的基于AI的BMI推断：重量监控的应用
AI-based BMI Inference from Facial Images: An Application to Weight Monitoring
论文作者
论文摘要
在肥胖的令人震惊的趋势之后，基于自我诊断的基于图像的方法正在增加兴趣。只有少数学术研究研究了基于AI的体重指数方法（BMI）推断，从面部图像作为解决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个不同深度学习的卷积神经网络（CNN）体系结构的性能，即VGG19，RESNET50，Densenet，Mobilenet和Lightcnn，用于BMI从面部图像中推断。关于从社交媒体组装的三个公开注释的面部图像数据集的实验结果，即Visualbmi，VIP-Attributes和Bollywood DataSet，这表明BMI推断中深度学习方法的功效是从面部图像中从面部图像中获得最小平均绝对误差（MAE）的功效，该图像的效率是1.04 $ $ 1.04 $。
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.
