Indian Masked Faces in the Wild Dataset

TL;DR

This paper introduces the Indian Masked Faces in the Wild (IMFW) dataset, capturing diverse masked face images in real-world Indian settings to evaluate and highlight the limitations of current face recognition algorithms.

Contribution

The paper presents a new culturally diverse masked face dataset from India and benchmarks existing face recognition models on it.

Findings

Existing algorithms perform poorly on the IMFW dataset.

The dataset includes diverse masks and conditions.

Current models need improvement for real-world masked face recognition.

Abstract

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.