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

arXiv: https://arxiv.org/abs/1902.10739 · HTML: https://mlsanity.com/paper/1902.10739/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [About Face: A Survey of Facial Recognition Evaluation](https://mlsanity.com/paper/2102.00813/) (2021)
- [Ethical Considerations for Responsible Data Curation](https://mlsanity.com/paper/2302.03629/) (2023)
- [Facial recognition technology and human raters can predict political orientation from images of expressionless faces even when controlling for demographics and self-presentation](https://mlsanity.com/paper/2303.16343/) (2023)
- [Measuring Trustworthiness or Automating Physiognomy? A Comment on Safra, Chevallier, Gr`ezes, and Baumard (2020)](https://mlsanity.com/paper/2202.08674/) (2022)
- [A Mental Trespass? Unveiling Truth, Exposing Thoughts and Threatening Civil Liberties with Non-Invasive AI Lie Detection](https://mlsanity.com/paper/2102.08004/) (2021)
- [Deep Learning and Ethics](https://mlsanity.com/paper/2305.15239/) (2023)
- [Post-hoc and manifold explanations analysis of facial expression data based on deep learning](https://mlsanity.com/paper/2404.18352/) (2024)

## Abstract (arXiv)

Recent research used machine learning methods to predict a person's sexual orientation from their photograph (Wang and Kosinski, 2017). To verify this result, 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 20,910 photographs from dating websites, the ability to predict sexual orientation is confirmed (DNN accuracy male 68%, female 77%, FM male 62%, female 72%). To investigate whether facial features such as brightness or predominant colours are predictive of sexual orientation, a new model based on highly blurred facial images was created. This model was also able to predict sexual orientation (male 63%, female 72%). The tested models are invariant to intentional 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 not correlated with sexual orientation. While demonstrating that dating profile images carry rich information about sexual orientation these results leave open the question of how much is determined by facial morphology and how much by differences in grooming, presentation and lifestyle. The advent of new technology that is able to detect sexual orientation in this way may have serious implications for the privacy and safety of gay men and women.