ArcFace Knows the Gender, Too!

TL;DR

This paper demonstrates that features extracted from ArcFace, a face recognition model, can be effectively used with traditional classifiers to accurately determine gender, achieving over 96% accuracy.

Contribution

The study shows that ArcFace features are highly discriminative for gender classification without needing a dedicated gender recognition model.

Findings

SVM with Gaussian kernel achieves 96.4% accuracy.

ArcFace features effectively distinguish gender classes.

Traditional classifiers perform well on ArcFace features for gender detection.

Abstract

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.