Previous studies found that sexual orientation can be detected from an individual’s digital footprints, such as social network structure (Jernigan & Mistree, 2009) or Facebook Likes (Kosinski, Stillwell, & Graepel, 2013). Such digital footprints, however, can be hidden, anonymized, or distorted. One’s face, on the other hand, cannot be easily concealed. A facial image can be easily taken and analyzed (e.g., with a smartphone or through CCTV). Facial images of billions of people are also stockpiled in digital and traditional archives, including dating platforms, photo-sharing websites, and government databases. Such pictures are often easily accessible; Facebook, LinkedIn, and Google Plus profile pictures, for instance, are public by default and can be accessed by anyone on the Internet.
Our findings suggest that such publicly available data and conventional machine learning tools could be employed to build accurate sexual orientation classifiers. As much of the signal seems to be provided by fixed morphological features, such methods could be deployed to detect sexual orientation without a person’s consent or knowledge. Moreover, the accuracies reported here are unlikely to constitute an upper limit of what is possible. Employing images of a higher resolution, larger numbers of images per person, larger training set, and more powerful DNN algorithms (e.g., He, Zhang, Ren, & Sun, 2015) could further boost accuracy.