Unsupervised Anomaly Appraisal of Cleft Faces Using a StyleGAN2-based Model Adaptation Technique. (arXiv:2211.06659v1 [cs.CV])

This paper presents a novel machine learning framework to consistently
detect, localize and rate congenital cleft lip anomalies in human faces. The
goal is to provide a universal, objective measure of facial differences and
reconstructive surgical outcomes that matches human judgments. The proposed
method employs the StyleGAN2 generative adversarial network with model
adaptation to produce normalized transformations of cleft-affected faces in
order to allow for subsequent measurement of deformity using a pixel-wise
subtraction approach. The complete pipeline of the proposed …

anomaly arxiv stylegan2 unsupervised