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.
Attention-based Gender Recognition on Masked Faces
Carletti V.;Greco A.;Saggese A.;Vento M.
2022-01-01
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
