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takyon writes:

Stanford University researchers have used software in an attempt to determine sexual orientation from photos:

"Deep neural networks are more accurate than humans at detecting sexual orientation from facial images" is the title of an article by Stanford University's Michal Kosinski and Yilun Wang, to be published in the Journal of Personality and Social Psychology. The abstract:

We show that faces contain much more information about sexual orientation than can be perceived and 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 74% 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.

The images and the sexual orientation information were drawn from an online dating site. Note that the study was limited to white people from the United States, because of the relative lack of images of nonwhite gays and lesbians on the site.

Also at TechCrunch, The Advocate, and The Guardian.

Original Submission