Coarse and Fine: A New Method for Gender Classification in the Wild

Abstract

As one of the most important soft biometrics, gender has substantial applications in various areas such as demography and human-computer interaction. Successful gender estimation of face images taken under real-world also contributes to improving the face identification results in the wild. However, most existing gender classification methods estimate gender under well controlled environment, which limits its implementation in real-world applications. In this paper, we propose a new network architecture to combine the coarse appearance features with delicate facial features for gender estimation task. We call this method “coarse and fine” to give a harsh description of the gender estimation process. Trained on the large scale uncontrolled CelebA dataset without any alignment, the proposed network tries to learn how to estimate gender of real-world face images. Cross-database experiments on LFWA and CASIA-WebFace dataset show the superiority of our proposed method.