  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.
