Hybrid facial image feature extraction and recognition for non-invasive chronic fatigue syndrome diagnosis
We proposed a hybrid facial feature based on clinical observations of TCM experts. Feature components are extracted from different facial regions with proper methods. Feature reduction and fusion are performed before prediction of CFS. The feature got an error rate less than 17.31% in cross-validation experiments. The whole method achieves an average accuracy of 88.32% for CFS prediction.
feature extraction chronic fatigue syndrome hybrid facial feature manifold preserving projection non-invasive cfs diagnosis