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Parametric face drawings: A demographically diverse and customizable face space model.
Jennifer Day1, Nicolas Davidenko1
1Department of Psychology, University of California, Santa Cruz, CA, USA.
Journal of Vision
|September 19, 2019
Summary
We developed Parametric Face Drawings (PFDs), a novel face space model using diverse data to create realistic facial line drawings. PFDs overcome limitations of existing models, showing reliable recognition effects and comparable performance to other methods.
Area of Science:
- Computer Vision
- Psychology
- Human-Computer Interaction
Background:
- Existing face space models often rely on ethnically homogeneous databases, negatively impacting recognition for underrepresented groups.
- This limitation hinders the development of equitable automated identity recognition systems.
Purpose of the Study:
- To introduce Parametric Face Drawings (PFDs), a novel face space model for generating schematic yet realistic parameterized face line drawings.
- To address the limitations of current models by utilizing a demographically diverse dataset.
Main Methods:
- Compiled a diverse dataset of 400 faces (balanced by gender and ethnicity).
- Manually coded 85 landmark points for each face image.
- Normalized and rendered landmark data using MATLAB tools to create smooth, parameterized face line drawings.
Main Results:
- Experiment 1 demonstrated that PFDs elicit a reliable "inversion effect" in short-term recognition, indicating holistic processing.
- Experiment 2 showed comparable celebrity face recognition rates (approx. 50%) for PFDs versus untextured renderings from FaceGen Modeller.
Conclusions:
- PFDs offer a viable alternative to existing face space models, particularly for research involving diverse populations.
- The model provides researchers with MATLAB resources for customization and generation of various facial representations.
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