Masked face analysis via multitask deep learning

Facial recognition with mask/noise has consistently been a challenging task in computer vision, which involves human wearing a facial mask. Masked Face Analysis via Multi-task deep learning is a method which will answer to many questions. In this thesis, we propose a unifying framework to simultaneously predict human age, gender, and emotions. This method is divided into three major steps; firstly, Creation of the dataset, Secondly, 3 individual classification models used for the system to learn the labelled (Age, Expression and Gender) images, Thirdly, the multi-task deep learning (MTDL) model; which takes the inputs as the data and shares their weight combined and gives the prediction of the person’s (with mask) age, expression and gender. However, this novel framework will give better output then the existing methods