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Faces contain more information about sexual orientation than can be perceived by the human brain. We used deep neural networks to extract features from over 35 thousand facial images.  Given a single facial image, a classifier could correctly distinguish between gay and heterosexual men in 80% of cases, and in 70% of cases for women. Accuracy increased to 90% and 80%, 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 detected with 55% and 53% accuracy for gay males and gay females, respectively. Such findings advance our understanding of the origins of sexual orientation and the limits of human perception. Given that organizations are 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.