Abstract
Digital Out Of Home (DOOH) applications which exploit computer vision algorithms to automatically collect soft biometrics of people in front a smart screen are of great interest for industry. In the last years many gender recognition pipelines have been proposed in literature. Different benchmark datasets have been introduced and used for testing purpose. This paper gives an overview of the state-of-the-art in the context of gender recognition by highlighting features, classifiers and datasets which can be employed to reach the goal. Comparisons of the results obtained by different approaches are also presented.