Mahdi, Soha SadatNauwelaers, NeleJoris, PhilipBouritsas, GiorgosGong, ShunwangWalsh, SusanShriver, Mark D.Bronstein, MichaelClaes, Peter2024-06-202024-06-202022-04Mahdi, S. S., Nauwelaers, N., Joris, P., Bouritsas, G., Gong, S., Walsh, S., Shriver, M. D., Bronstein, M., & Claes, P. (2022). Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach. IEEE Transactions on Biometrics, Behavior, and Identity Science, 4(2), 163–172. https://doi.org/10.1109/TBIOM.2021.3092564https://hdl.handle.net/1805/41669Face 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 is to match the 3D face of an individual to a set of demographic properties (sex, age, BMI, and genomic background) that are extracted from unidentified genetic material. We 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, spiral convolutions are used along with a novel mesh-sampling scheme, which retains uniformly sampled points at different resolutions. The capacity of the model for establishing identity from facial shape against a list of probe demographics is evaluated 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.en-USPublisher PolicyDeep Metric LearningFace to DNAGeometric Deep LearningMulti BiometricsSoft BiometricsArticle