Attention-based Gender Recognition on Masked Faces

Gender recognition from face images can be profitably used in several vertical markets, such as targeted advertising and cognitive robotics. However, in the last years, due to the COVID-19 pandemic, the unreliability of such systems when dealing with faces covered by a mask has emerged. In this paper, we propose a novel architecture based on attention layers and trained with a domain specific data augmentation technique for reliable gender recognition of masked faces. The proposed method has been experimentally evaluated on a huge dataset, namely VGGFace2-M, a masked version of the well known VGGFace2 dataset, and the achieved results confirm an improvement of around 4% with respect to traditional gender recognition algorithms, while preserving the performance on unmasked faces.