ArcFace Knows the Gender, Too!
This is an incremental improvement for researchers in facial analysis, as it applies existing methods to a new task without introducing novel techniques.
The paper tackled gender classification by using ArcFace facial recognition features with traditional machine learning models, achieving 96.4% accuracy using SVM with a Gaussian kernel on a gender classification dataset.
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.