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                            Library & Multimedia
 Matching 3D Facial Shape to Demographic Properties by Geometric Metric Learning: A Part-Based Approach
NCJ Number
              307358

Author(s)
          Soha Sadat Mahdi;
                        Nele Nauwelaers;
                        Philip Joris;
                        Giorgos Bouritsas;
                        Shunwang Gong;
                        Susan Walsh;
                        Mark D. Shriver;
                        Michael Bronstein;
                        Peter Claes
Date Published

April 2022
Length
              10 pages
Annotation
              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.
Abstract
              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
      
  
          Additional Details
Grant Number(s)
        2018-DU-BX-0219
                                2014-DN-BX-K031


  
  
        
  Agencies
        NIJ-Sponsored


  
  
        
  Publication Type
        Research (Applied/Empirical)


  
  
        
  Award
        Improving the prediction of human quantitative pigmentation traits such as eye, hair and skin color using a worldwide representation panel of US and European individuals
                                Ensuring standards and enhancing the field of predictive biometrics using a globally-diverse genotype-phenotype database
    
  
  
Date Published: April 1, 2022
    
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        DNA (Deoxyribonucleic Acid)



  
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