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. Keywords: sexual orientation, facial morphology, prenatal hormone theory, computational social science, privacy Supplemental materials: http://dx.doi.org/10.1037/pspa0000098.supp The science of judging one's character from their facial characteristics, or physiognomy, dates back to ancient China and Greece (Jenkinson, 1997) . Aristotle and Pythagoras were among its disciples, and the latter is said to have selected his students based on their facial features (Riedweg, 2005) . Such beliefs have persisted and grown in popularity over the centuries. Robert FitzRoy, the captain of the Beagle, believed that Darwin's nose revealed a lack of energy and determination, and was close to barring him from the historic voyage (Glaser, 2002) . Cesare Lombroso, the founder of criminal anthropology, believed that criminals could be identified by their facial features. He claimed, for example, that arsonists have a "softness of skin, an almost childlike appearance, and an abundance of thick straight hair that is almost feminine" (Lombroso, 1911, p. 51). By the 18th century, physiognomy "was not merely a popular fad but also the subject of intense academic debate about the promises it held for future progress" (Porter, 2003, p. 497). Physiognomy is now universally, and rightly, rejected as a mix of superstition and racism disguised as science (Jenkinson, 1997) . Due to its legacy, studying or even discussing the links between facial features and character became taboo, leading to a widespread presumption that no such links exist. However, there are many demonstrated mechanisms that imply the opposite. Such mechanisms can be arranged into three groups. First, there is much evidence that character can influence one's facial appearance (e.g., Lõhmus, Sundström, & Björklund, 2009; Zebrowitz & Collins, 1997) . For example, women that scored high on extroversion early in life tend to become more attractive with age (Zebrowitz, Collins, & Dutta, 1998) . Second, facial appearance can alter one's character. Facial appearance drives first impressions of others, influencing our expectations and behavior toward them, which, in turn, shapes their character (Berry, 1991; Berry & Brownlow, 1989; Penton-Voak, Pound, Little, & Perrett, 2006; Todorov, Said, Engell, & Oosterhof, 2008; Zebrowitz & Collins, 1997 The study has been approved by Stanford University's IRB. We thank Samuel Gosling, Robert Sternberg, Raphael Silberzahn, Martie Haselton, Amir Goldberg, Poruz Khambatta, Anonymous Gabriella, Jason Rentfrow, Kai Ruggeri, Pierre Dechant, Brent Roberts, David Mack, and Nicole Paulk for their critical reading of the earlier version of this article. Also, we thank Isabelle Abraham for proofreading and Mariia Vorobiova for graphical design. Finally, we would like to thank the creators of Faceϩϩ for allowing us to use their software free of charge.
Wang and Kosinski REF trained a deep neural network to classify facial images based on sexual orientation.
Deep Neural Networks Are More Accurate Than Humans at Detecting Sexual Orientation From Facial Images
Journal of Personality and Social Psychology
Psychology Medicine