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Masked Face Analysis via Multi-Task Deep Learning.

Vatsa S Patel¹, Zhongliang Nie¹, Trung-Nghia Le²

¹Department of Computer Science, University of Dayton, Dayton, OH 45469, USA.

Journal of Imaging

|October 22, 2021

Summary

This summary is machine-generated.

This study introduces a new framework for analyzing masked faces, predicting age, gender, and emotions. The multi-task deep learning model achieves better performance than existing methods for masked face recognition.

Keywords:

age expression face detection gender masked face multi-task learning

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Face recognition is challenged by wearable items, particularly face masks.
Masked face analysis is crucial for various applications.
Multi-task learning can enhance performance in face analysis tasks.

Purpose of the Study:

To propose a unified framework for masked face analysis.
To predict age, gender, and emotions from masked faces.
To introduce a new dataset, FGNET-MASK, for masked face recognition.

Main Methods:

Developed a unified framework for masked face analysis.
Constructed the FGNET-MASK dataset.
Proposed a multi-task deep learning model that shares weights for predicting age, gender, and emotions.

Main Results:

The proposed multi-task deep learning model effectively predicts age, gender, and emotions from masked faces.
Extensive experiments demonstrated superior performance compared to existing methods.
The FGNET-MASK dataset facilitates research in masked face recognition.

Conclusions:

The unified framework and multi-task learning approach significantly advance masked face analysis.
The proposed model offers improved accuracy for age, gender, and emotion prediction in masked individuals.
This work provides a valuable contribution to the field of computer vision and face recognition.