Each front-view face image is manually coded with 85 landmark points that are then normalized and rendered with MATLAB (MathWorks, Natick, MA) tools to produce a smooth, parameterized face line drawing. Our model is based on a demographically diverse sample of 400 faces (200 female, 200 male 100 East Asian/Pacific Islander, 100 Latinx/Hispanic, 100 black/African-American, and 100 white/Caucasian) compiled from several face databases (including FERET face recognition technology and the Chicago Face Database). This constraint has led to negative consequences for underrepresented populations, such as impairments in automatized identity recognition of certain demographic groups. A review of existing face space models (including FaceGen Modeller, Synthetic Faces, MPI, and active appearance model) indicates that current models are constrained by their reliance on ethnically homogeneous face databases. We introduce a novel face space model-parametric face drawings (or PFDs)-to generate schematic, though realistic, parameterized line drawings of faces based on the statistical distribution of human facial features.
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