  Due to the COVID-19 pandemic, wearing face masks has become a mandate in
public places worldwide. Face masks occlude a significant portion of the facial
region. Additionally, people wear different types of masks, from simple ones to
ones with graphics and prints. These pose new challenges to face recognition
algorithms. Researchers have recently proposed a few masked face datasets for
designing algorithms to overcome the challenges of masked face recognition.
However, existing datasets lack the cultural diversity and collection in the
unrestricted settings. Country like India with attire diversity, people are not
limited to wearing traditional masks but also clothing like a thin cotton
printed towel (locally called as ``gamcha''), ``stoles'', and ``handkerchiefs''
to cover their faces. In this paper, we present a novel \textbf{Indian Masked
Faces in the Wild (IMFW)} dataset which contains images with variations in
pose, illumination, resolution, and the variety of masks worn by the subjects.
We have also benchmarked the performance of existing face recognition models on
the proposed IMFW dataset. Experimental results demonstrate the limitations of
existing algorithms in presence of diverse conditions.
