PatchBMI-Net: Lightweight Facial Patch-based Ensemble for BMI Prediction
This work addresses the need for efficient on-device BMI monitoring via smartphones, offering a lightweight solution that is incremental over existing CNN-based methods.
The paper tackles the problem of deploying facial image-based BMI prediction on resource-constrained mobile devices by proposing PatchBMI-Net, a lightweight ensemble model that achieves a Mean Absolute Error (MAE) in the range [3.58, 6.51] with about 3.3 million parameters, reducing model size by 5.4x and inference time by 3x compared to heavyweight models like ResNet-50.
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.