# ArcFace Knows the Gender, Too!

arXiv: https://arxiv.org/abs/2112.10101 · HTML: https://mlsanity.com/paper/2112.10101/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 21 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 5 of 5)

- [MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition](https://mlsanity.com/paper/1607.08221/) (2016)
- [An All-In-One Convolutional Neural Network for Face Analysis](https://mlsanity.com/paper/1611.00851/) (2016)
- [Real-time Convolutional Neural Networks for Emotion and Gender Classification](https://mlsanity.com/paper/1710.07557/) (2017)
- [Deep Convolutional Neural Network for Age Estimation based on VGG-Face Model](https://mlsanity.com/paper/1709.01664/) (2017)
- [Does Face Recognition Error Echo Gender Classification Error?](https://mlsanity.com/paper/2104.13803/) (2021)

## Abstract (arXiv)

The main idea of this paper is that if a model can recognize a person, of course, it must be able to know the gender of that person, too. Therefore, instead of defining a new model for gender classification, this paper uses ArcFace features to determine gender, based on the facial features. A face image is given to ArcFace and 512 features are obtained for the face. Then, with the help of traditional machine learning models, gender is determined. Discriminative methods such as Support Vector Machine (SVM), Linear Discriminant, and Logistic Regression well demonstrate that the features extracted from the ArcFace create a remarkable distinction between the gender classes. Experiments on the Gender Classification Dataset show that SVM with Gaussian kernel is able to classify gender with an accuracy of 96.4% using ArcFace features.