Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach

Soha Sadat Mahdi, Nele Nauwelaers*, Philip Joris, Giorgos Bouritsas, Shunwang Gong, Susan Walsh, Mark D. Shriver, Michael Bronstein, Peter Claes

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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 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.

Original languageEnglish
Pages (from-to)163-172
Number of pages10
JournalIEEE Transactions on Biometrics, Behavior, and Identity Science
Issue number2
StatePublished - 1 Apr 2022
Externally publishedYes


  • Deep metric learning
  • face to DNA
  • geometric deep learning
  • multi biometrics
  • soft biometrics


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