Deep neural networks are more accurate than humans at detecting sexual orientation from facial images

Bibliographic Data

ID6204074
AuthorsYilun Wang (0000-0002-9468-4110, Stanford University), Michal Kosinski (0000-0003-2936-4775, Stanford University)
Year2018
Volume114
Issue2
Pages246-257
Publication date2018-02-01
Peer ReviewedYes
Open AccessNo
TypeARTICLE
VenueJournal of Personality and Social Psychology (JOURNAL)
Journal identifiersISSN: 0022-3514 • E-ISSN: 1939-1315
PublisherAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037/pspa0000098
PMID29389215
OpenAlexW2781265533
LanguageEN
Citations received95

We show that faces contain much more information about sexual orientation than can be perceived or interpreted by the human brain. We used deep neural networks to extract features from 35,326 facial images. These features were entered into a logistic regression aimed at classifying sexual orientation. Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 81% of cases, and in 71% of cases for women. Human judges achieved much lower accuracy: 61% for men and 54% for women. The accuracy of the algorithm increased to 91% and 83%, respectively, given five facial images per person. Facial features employed by the classifier included both fixed (e.g., nose shape) and transient facial features (e.g., grooming style). Consistent with the prenatal hormone theory of sexual orientation, gay men and women tended to have gender-atypical facial morphology, expression, and grooming styles. Prediction models aimed at gender alone allowed for detecting gay males with 57% accuracy and gay females with 58% accuracy. Those findings advance our understanding of the origins of sexual orientation and the limits of human perception. Additionally, given that companies and governments are increasingly using computer vision algorithms to detect people's intimate traits, our findings expose a threat to the privacy and safety of gay men and women. (PsycINFO Database Record

Classifier (UML) · Cognitive psychology · Developmental psychology · Face perception · Facial expression · Logistic regression · Machine learning · Orientation (vector space) · Perception · Sexual orientation · Social perception · Artificial Intelligence · Communication · Computer Science · Demographic Trends and Gender Preferences · Evolutionary Psychology and Human Behavior · Psychology · Social Psychology

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