Unsupervised gender prediction based on deep facial features
Mai Tuong, Le Thanh Dat, Vinh Truong Hoang, Tram-Anh Nguyen-Thi · 2021
Gender prediction is a common topic in machine learning. It can be seen that many approaches have been proposed for this task and achieved certain successes. However, it is necessary to figure out a method that has high performance and reduce time processing. This paper focuses on applying deep neural networks to solve gender prediction based on facial images. Specifically, Convolutional Neural Networks (CNN) and its enhancements are applied to extract features of facial images. Then, K-means clustering is employed to predict gender on a large-scale GenderFace80K dataset with 80,000 facial images with gender annotation.