Gender classification on 2D human skeleton
Paola Barra, Carmen Bisogni, Michele Nappi, David Freire-Obregón, Modesto Castrillón-Santana · 2019
Soft bimetrics has become a trending research topic over the past decade. In last years, the increase of new technologies such as the wearable camera devices has introduced a new challenge into the gender classification problem. In this sense, the ability to classify the gender not by an image but by the 2D estimated skeleton points is considered in this paper. Our experiments show that the human gender can be classified just considering the pose information provided by the body pose information. The proposed method have shown a remarkable performance on a dataset where subjects and camera are in movement.