Researchers from Stanford claim they have created an algorithm that can predict sexual orientation with incredibly high accuracy using facial detection technology.
The study, titled “Deep neural networks are more accurate than humans at detecting sexual orientation from facial images”, was published this week.
The researchers analysed 35,326 facial images, using deep neural networks to extract features from the images, which were taken from a popular online dating site.
These features were then entered into a “logistic regression” tool designed to classify the images by sexual orientation.
Researchers Michal Kosinski and Yilun Wang claim that the algorithm they built could correctly distinguish between gay and heterosexual men in 81% of cases, and in 74% of cases for women.
This is compared to the judgement of humans, which was found to be accurate in 61% of cases for men and 54% for women.
The researchers even found that the accuracy of the algorithm increased to 91% and 83% when it was given five images of each person’s face.
Explaining their findings, the Stanford researchers said: “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.”
In addition to this, Michal Kosinski and Yilun Wang said their results provides strong support for the belief that sexual orientation stems from people being exposed to certain hormones prior to birth.
Read more about the fascinating study here.