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Dual Sensor Indian Masked Face Dataset

With the advancements in deep learning technologies, real-world applications like face detection, gender prediction, and face recognition have achieved human-level performance. However, the emergence of the COVID-19 pandemic brought new challenges to existing deep learning algorithms. People are forced to wear a mask to limit the spread of COVID-19. These face masks occlude a significant portion of the face, thereby posing multiple challenges to existing algorithms. Images captured using surveillance cameras have a low resolution which hinders the model performance. Along with this, skin tone, ethnicity and attire also play a significant role in detection and recognition performance. India is a large country with huge diversity in skin tone and attire of the people. To address the challenges due to masks in the Indian context, we propose a novel Dual Sensor Indian Masked Face (DS- IMF) dataset, which contains images captured in constrained environmental settings with a variety of masks and degrees of occlusion. Multiple experiments are performed on the DS- IMF dataset at different resolutions. Experimental results demonstrate the limitations of existing algorithms on low-resolution masked face images. The proposed dataset can be found at http://www.iab-rubric.org/resources/dsimf.html.