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Item 3D Facial Matching by Spiral Convolutional Metric Learning and a Biometric Fusion-Net of Demographic Properties(IEEE, 2021) Mahdi, Soha Sadat; Nauwelaers, Nele; Joris, Philip; Bouritsas, Giorgos; Gong, Shunwang; Bokhnyak, Sergiy; Walsh, Susan; Shriver, Mark D.; Bronstein, Michael; Claes, Peter; Biology, School of ScienceFace 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 to-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 Naïve Bayes-based score-fuser.Item A Longitudinal Imaging Genetics Study of Neuroanatomical Asymmetry in Alzheimer's Disease(Elsevier, 2018-10-01) Wachinger, Christian; Nho, Kwangsik; Saykin, Andrew J.; Reuter, Martin; Rieckmann, Anna; Alzheimer’s Disease Neuroimaging Initiative; Radiology and Imaging Sciences, School of MedicineNeuroanatomical asymmetries have recently been associated with the progression of Alzheimer’s disease (AD) but the biological basis of asymmetric brain changes in disease remains unknown. Methods We investigated genetic influences on brain asymmetry by identifying associations between MRI-derived measures of asymmetry and candidate single-nucleotide polymorphisms (SNPs) that have previously been identified in genome-wide association studies (GWAS) for AD diagnosis and for brain subcortical volumes. For the longitudinal neuroimaging data (1,241 individuals; 6,395 scans), we use a mixed effects model with interaction between genotype and diagnosis. Results We found significant associations between asymmetry of amygdala, hippocampus, and putamen and SNPs in the genes BIN1, CD2AP, ZCWPW1, ABCA7, TNKS, and DLG2. For AD candidate SNPs, we demonstrated an asymmetric effect on subcortical brain structures. Conclusions The associations between SNPs in the genes TNKS and DLG2 and AD-related increases in shape asymmetry are of particular interest; these SNPs have previously been associated with subcortical volumes of amygdala and putamen but have not yet been associated with Alzheimer’s pathology. This provides novel evidence about the biological underpinnings of brain asymmetry as a disease marker. Contralateral brain structures represent a unique, within-patient, reference element for disease and asymmetries can provide a personalized measure of the accumulation of past disease processes.