Masked Face Analysis via Multi-Task Deep Learning

Author(s)

• Vatsa S. Patel, University of DaytonFollow
• Zhongliang Nie, University of DaytonFollow
• Trung-Nghia Le, National Institute of InformaticsFollow
• Tam V. Nguyen, University of DaytonFollow

Document Type

Article

Publication Date

10-5-2021

Publication Source

Journal of Imaging

Abstract

Face recognition with wearable items has been a challenging task in computer vision and involves the problem of identifying humans wearing a face mask. Masked face analysis via multi-task learning could effectively improve performance in many fields of face analysis. In this paper, we propose a unified framework for predicting the age, gender, and emotions of people wearing face masks. We first construct FGNET-MASK, a masked face dataset for the problem. Then, we propose a multi-task deep learning model to tackle the problem. In particular, the multi-task deep learning model takes the data as inputs and shares their weight to yield predictions of age, expression, and gender for the masked face. Through extensive experiments, the proposed framework has been found to provide a better performance than other existing methods.

ISBN/ISSN

2313-433X

Comments

Article is licensed under the Creative Commons Attribution License (CC-BY)

Publisher

Imaging

Volume

7

Peer Reviewed

true

Issue

10

Keywords

multi-task learning, masked face, age, gender, expression, face detection

Sponsoring Agency

This work was supported by the National Science Foundation (NSF) under Grant 2025234, the Japan Society for the Promotion of Science (JSPS) KAKENHI Grants JP20K23355 and JP21K18023.

eCommons Citation

Patel, Vatsa S.; Nie, Zhongliang; Le, Trung-Nghia; and Nguyen, Tam V., "Masked Face Analysis via Multi-Task Deep Learning" (2021). Computer Science Faculty Publications. 214.
https://ecommons.udayton.edu/cps_fac_pub/214