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Published on: June 3, 2013
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Face dissimilarity judgments are predicted by representational distance in morphable and image-computable models
Kamila M Jozwik1, Jonathan O'Keeffe2, Katherine R Storrs3,4
1Department of Psychology, University of Cambridge, Cambridge CB2 3EB, United Kingdom.
Summary
Three-dimensional (3D) morphable face models effectively predict human perception of facial similarity and identity. This suggests human face representations align with the statistical distribution of real-world faces.
Area of Science:
- Cognitive Science
- Computer Vision
- Psychology
Background:
- Human vision excels at discerning subtle facial differences.
- Quantitative prediction of face similarity and identity remains a challenge.
- Three-dimensional (3D) morphable models (e.g., Basel Face Model [BFM]) are standard for generating face stimuli in research.
Purpose of the Study:
- To assess how well a face space derived from 3D morphable models captures human perception of face similarity and identity.
- To compare the predictive power of the 3D morphable model face space against various computational models.
Main Methods:
- A behavioral study collected dissimilarity and same/different identity judgments for 232 face pairs.
- Stimuli were generated using geometric relationships within the BFM face space.
- Multiple computational models, including BFM, active appearance models, and deep neural networks (DNNs), were evaluated for predictive accuracy.
Main Results:
- Euclidean distance within the BFM face space accurately predicted both perceived face dissimilarity and identity judgments.
- BFM distance performance was competitive with state-of-the-art DNNs.
- Novel DNNs trained on BFM data also showed strong predictive capabilities.
Conclusions:
- 3D morphable models not only generate realistic face stimuli but also capture essential aspects of human face perception.
- Human face representations appear to be tuned to the statistical distribution of faces in the real world.
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