Due to an alarming trend related to obesity affecting 93.3 million adults in the United States alone, body mass index (BMI) and body weight have drawn significant interest in various health monitoring applications. Consequently, several studies have proposed self-diagnostic facial image-based BMI prediction methods for healthy weight monitoring. These methods have mostly used convolutional neural network (CNN) based regression baselines, such as VGG19, ResNet50, and Efficient-NetB0, for BMI prediction from facial images. However, the high computational requirement of these heavy-weight CNN models limits their deployment to resource-constrained mobile devices, thus deterring weight monitoring using smartphones. This paper aims to develop a lightweight facial patch-based ensemble (PatchBMI-Net) for BMI prediction to facilitate the deployment and weight monitoring using smartphones. Extensive experiments on BMI-annotated facial image datasets suggest that our proposed PatchBMI-Net model can obtain Mean Absolute Error (MAE) in the range [3.58, 6.51] with a size of about 3.3 million parameters. On cross-comparison with heavyweight models, such as ResNet-50 and Xception, trained for BMI prediction from facial images, our proposed PatchBMI-Net obtains equivalent MAE along with the model size reduction of about 5.4x and the average inference time reduction of about 3x when deployed on Apple-14 smartphone. Thus, demonstrating performance efficiency as well as low latency for on-device deployment and weight monitoring using smartphone applications.

本文旨在开发一种轻量级面部补丁集成方法（PatchBMI-Net）进行BMI预测，以便在智能手机上进行部署和体重监控。大量的实验证明，我们提出的PatchBMI-Net模型在参数数量为约330万的情况下，可以在MAE范围为[3.58, 6.51]之间获得最佳结果。在与ResNet-50和Xception等重型模型进行对比时，我们提出的PatchBMI-Net在苹果14手机上的部署中，获得了相当的MAE，并实现了模型尺寸减小约5.4倍和平均推断时间减少约3倍的效果，从而在设备端部署和智能手机应用中实现了性能效率和低延迟的体重监控。

PatchBMI-Net: 基于面部块的轻量级集成模型用于BMI预测