Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.

Volume: 114, Issue: 2, Pages: 246 - 257
Published: Feb 1, 2018
Abstract
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...
Paper Details
Title
Deep neural networks are more accurate than humans at detecting sexual orientation from facial images.
Published Date
Feb 1, 2018
Volume
114
Issue
2
Pages
246 - 257
Citation AnalysisPro
  • Scinapse’s Top 10 Citation Journals & Affiliations graph reveals the quality and authenticity of citations received by a paper.
  • Discover whether citations have been inflated due to self-citations, or if citations include institutional bias.