Cascade of convolutional models for few-shot automatic cephalometric landmarks localization

Cephalometric landmarks are used in many forensic tasks of great relevance. Nevertheless, the automatic localization of such points is greatly underdeveloped in the scientific literature, especially on in-the-wild images where no published work is available. Inspired by state-of-the-art automatic facial landmark localization research, we present a method based on a cascade of conditional convolutional networks for predicting high-resolution cephalometric landmarks under specific conditions: using a size-limited dataset of in-the-wild images usually handled by forensic anthropologists. Every contribution is thoroughly ablated and validated. We compare our proposal against top-performing standard facial landmark localization methods. Furthermore, we conduct a user study comparing our performance against expert annotators on a different problem-specific dataset. The results show that we outperform competing methods in a cephalometric landmarks dataset by a large margin, two times better than the closest one, and achieve human-like performance in half of the cases. For its good results, our proposal will be included in Skeleton-ID, a commercial solution for forensic identification assisted by artificial intelligence.