Masked Face Analysis via Multitask Learning

Authors

Presenter(s)

Vatsa Sanjay Patel

Comments

This poster reflects research conducted as part of a course project designed to give students experience in the research process. Course: CPS 599

Files

Description

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 learning is a method which will answer to many questions. In this paper, 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 learning (MTL) 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.

Publication Date

4-22-2021

Project Designation

Course Project

Primary Advisor

Van Tam Nguyen

Primary Advisor's Department

Computer Science

Keywords

Stander Symposium project, College of Arts and Sciences

United Nations Sustainable Development Goals

Industry, Innovation, and Infrastructure

Recommended Citation

"Masked Face Analysis via Multitask Learning" (2021). Stander Symposium Projects. 2147.
https://ecommons.udayton.edu/stander_posters/2147