  In Traditional Chinese Medicine (TCM), facial features are important basis
for diagnosis and treatment. A doctor of TCM can prescribe according to a
patient's physical indicators such as face, tongue, voice, symptoms, pulse.
Previous works analyze and generate prescription according to symptoms.
However, research work to mine the association between facial features and
prescriptions has not been found for the time being. In this work, we try to
use deep learning methods to mine the relationship between the patient's face
and herbal prescriptions (TCM prescriptions), and propose to construct
convolutional neural networks that generate TCM prescriptions according to the
patient's face image. It is a novel and challenging job. In order to mine
features from different granularities of faces, we design a multi-scale
convolutional neural network based on three-grained face, which mines the
patient's face information from the organs, local regions, and the entire face.
Our experiments show that convolutional neural networks can learn relevant
information from face to prescribe, and the multi-scale convolutional neural
networks based on three-grained face perform better.
