  Predicting attributes in the landmark free facial images is itself a
challenging task which gets further complicated when the face gets occluded due
to the usage of masks. Smart access control gates which utilize identity
verification or the secure login to personal electronic gadgets may utilize
face as a biometric trait. Particularly, the Covid-19 pandemic increasingly
validates the essentiality of hygienic and contactless identity verification.
In such cases, the usage of masks become more inevitable and performing
attribute prediction helps in segregating the target vulnerable groups from
community spread or ensuring social distancing for them in a collaborative
environment. We create a masked face dataset by efficiently overlaying masks of
different shape, size and textures to effectively model variability generated
by wearing mask. This paper presents a deep Multi-Task Learning (MTL) approach
to jointly estimate various heterogeneous attributes from a single masked
facial image. Experimental results on benchmark face attribute UTKFace dataset
demonstrate that the proposed approach supersedes in performance to other
competing techniques. The source code is available at
https://github.com/ritikajha/Attribute-prediction-in-masked-facial-images-with-deep-multitask-learning
