TY - GEN
T1 - Learning a face space for experiments on human identity
AU - Suchow, Jordan W.
AU - Peterson, Joshua C.
AU - Griffiths, Thomas L.
N1 - Publisher Copyright:
© 2018 Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018. All rights reserved.
PY - 2018
Y1 - 2018
N2 - Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed “police sketches” in a small number of trials.
AB - Generative models of human identity and appearance have broad applicability to behavioral science and technology, but the exquisite sensitivity of human face perception means that their utility hinges on the alignment of the model's representation to human psychological representations and the photorealism of the generated images. Meeting these requirements is an exacting task, and existing models of human identity and appearance are often unworkably abstract, artificial, uncanny, or biased. Here, we use a variational autoencoder with an autoregressive decoder to learn a face space from a uniquely diverse dataset of portraits that control much of the variation irrelevant to human identity and appearance. Our method generates photorealistic portraits of fictive identities with a smooth, navigable latent space. We validate our model's alignment with human sensitivities by introducing a psychophysical Turing test for images, which humans mostly fail. Lastly, we demonstrate an initial application of our model to the problem of fast search in mental space to obtain detailed “police sketches” in a small number of trials.
KW - face recognition
KW - generative models
KW - images
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85139557200&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85139557200&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85139557200
T3 - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
SP - 2548
EP - 2553
BT - Proceedings of the 40th Annual Meeting of the Cognitive Science Society, CogSci 2018
PB - The Cognitive Science Society
T2 - 40th Annual Meeting of the Cognitive Science Society: Changing Minds, CogSci 2018
Y2 - 25 July 2018 through 28 July 2018
ER -