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Recent research used machine learning methods to predict a person's sexualorientation from their photograph (Wang and Kosinski, 2017). To verify thisresult, two of these models are replicated, one based on a deep neural network(DNN) and one on facial morphology (FM). Using a new dataset of 20,910photographs from dating websites, the ability to predict sexual orientation isconfirmed (DNN accuracy male 68%, female 77%, FM male 62%, female 72%). Toinvestigate whether facial features such as brightness or predominant coloursare predictive of sexual orientation, a new model based on highly blurredfacial images was created. This model was also able to predict sexualorientation (male 63%, female 72%). The tested models are invariant tointentional changes to a subject's makeup, eyewear, facial hair and head pose(angle that the photograph is taken at). It is shown that the head pose is notcorrelated with sexual orientation. While demonstrating that dating profileimages carry rich information about sexual orientation these results leave openthe question of how much is determined by facial morphology and how much bydifferences in grooming, presentation and lifestyle. The advent of newtechnology that is able to detect sexual orientation in this way may haveserious implications for the privacy and safety of gay men and women.

A Replication Study: Machine Learning Models Are Capable of Predicting Sexual Orientation From Facial Images