This paper presents research and the development of a method for allowing a global-to-local part-based analysis of the face; the authors report on their evaluation of the capacity of the model for establishing identity from facial shape against a list of probe demographics; and they discuss the results of their efforts.
Face recognition is a widely accepted biometric identifier, as the face contains a lot of information about the identity of a person. The goal of this study was to match the 3D face of an individual to a set of demographic properties (sex, age, body-mass index (BMI), and genomic background) that are extracted from unidentified genetic material. In this document, the authors introduce a triplet loss metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. The metric learner is trained for multiple facial segments to allow a global-to-local part-based analysis of the face. To learn directly from 3D mesh data, the authors used spiral convolutions along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The authors evaluate the capacity of the model for establishing identity from facial shape against a list of probe demographics by enrolling the embeddings for all properties into a support vector machine classifier or regressor and then combining them using a naive Bayes score fuser. Results obtained by a 10-fold cross-validation for biometric verification and identification show that part-based learning significantly improves the systems performance for both encoding with our geometric metric learner or with principal component analysis. Publisher Abstract Provided