SEXNET: A Neural Network Identifies Sex from Human Faces
Abstract
Sex identification in animals has biological importance. Humans are good at making this determination visually, but machines have not matched this ability. A neural network was trained to discriminate sex in human faces, and performed as well as humans on a set of 90 exemplars. Images sampled at 30x30 were compressed using a 900x40x900 fully-connected back-propagation network; activities of hidden units served as input to a back-propagation "SexNet" trained to produce values of 1 for male and o for female faces. The network's average error rate of 8.1% compared favorably to humans, who averaged 11.6%. Some SexNet errors mimicked those of humans.
Cite
Text
Golomb et al. "SEXNET: A Neural Network Identifies Sex from Human Faces." Neural Information Processing Systems, 1990.Markdown
[Golomb et al. "SEXNET: A Neural Network Identifies Sex from Human Faces." Neural Information Processing Systems, 1990.](https://mlanthology.org/neurips/1990/golomb1990neurips-sexnet/)BibTeX
@inproceedings{golomb1990neurips-sexnet,
title = {{SEXNET: A Neural Network Identifies Sex from Human Faces}},
author = {Golomb, B.A. and Lawrence, D.T. and Sejnowski, T.J.},
booktitle = {Neural Information Processing Systems},
year = {1990},
pages = {572-577},
url = {https://mlanthology.org/neurips/1990/golomb1990neurips-sexnet/}
}