  Face recognition is a widely accepted biometric verification tool, as the
face contains a lot of information about the identity of a person. In this
study, a 2-step neural-based pipeline is presented for matching 3D facial shape
to multiple DNA-related properties (sex, age, BMI and genomic background). The
first step consists of a triplet loss-based metric learner that compresses
facial shape into a lower dimensional embedding while preserving information
about the property of interest. Most studies in the field of metric learning
have only focused on 2D Euclidean data. In this work, geometric deep learning
is employed to learn directly from 3D facial meshes. To this end, spiral
convolutions are used along with a novel mesh-sampling scheme that retains
uniformly sampled 3D points at different levels of resolution. The second step
is a multi-biometric fusion by a fully connected neural network. The network
takes an ensemble of embeddings and property labels as input and returns
genuine and imposter scores. Since embeddings are accepted as an input, there
is no need to train classifiers for the different properties and available data
can be used more efficiently. Results obtained by a 10-fold cross-validation
for biometric verification show that combining multiple properties leads to
stronger biometric systems. Furthermore, the proposed neural-based pipeline
outperforms a linear baseline, which consists of principal component analysis,
followed by classification with linear support vector machines and a Naive
Bayes-based score-fuser.
