ArticleScientific reports2023
Using deep learning to predict ideology from facial photographs: expressions, beauty, and extra-facial information.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Masked autoencoder of multi-scale convolution strategy combined with knowledge distillation for facial beauty prediction.Scientific reports · 2025Article
- What is beautiful is still good: the attractiveness halo effect in the era of beauty filters.Royal Society open science · 2024Article
- Automated classification in turtles genus Malayemys using ensemble multiview image based on improved YOLOv8 with CNN.Scientific reports · 2024Article
- AI for Psychometrics: Validating Machine Learning Models in Measuring Emotional Intelligence with Eye-Tracking Techniques.Journal of Intelligence · 2023Article
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Authors and funding
3 authors.
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Abstract
Deep learning techniques can use public data such as facial photographs to predict sensitive personal information, but little is known about what information contributes to the predictive success of these techniques. This lack of knowledge limits both the public's ability to protect against revealing unintended information as well as the scientific utility of deep learning results. We combine convolutional neural networks, heat maps, facial expression coding, and classification of identifiable features such as masculinity and attractiveness in our study of political ideology in 3323 Danes. Predictive accuracy from the neural network was 61% in each gender. Model-predicted ideology correlated with aspects of both facial expressions (happiness vs neutrality) and morphology (specifically, attractiveness in females). Heat maps highlighted the informativeness of areas both on and off the face, pointing to methodological refinements and the need for future research to better understand the significance of certain facial areas.
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