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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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A Review on Face Mask Recognition.

Jiaonan Zhang¹, Dong An², Yiwen Zhang³

¹School of Information and Communications Engineering, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)

|January 25, 2025

Summary

This summary is machine-generated.

This review categorizes face mask detection methods, highlighting the importance of diverse datasets and multimodal data for robust performance in public health applications. Emerging solutions focus on efficiency and privacy.

Keywords:

COVID-19 face mask detection object detection

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Area of Science:

Computer Science
Artificial Intelligence
Public Health Technology

Background:

Face mask detection is crucial for public health, especially during pandemics.
Technological advancements are vital for effective public health interventions.
Accurate detection systems support disease control and prevention strategies.

Purpose of the Study:

To provide a comprehensive analysis of face mask detection and recognition technologies.
To categorize existing detection methods and evaluate their strengths and limitations.
To explore the role of datasets and emerging solutions in improving detection systems.

Main Methods:

Systematic categorization of detection methods into feature-extraction-and-classification, object-detection-models, and multi-sensor-fusion approaches.
Detailed comparison of workflows, strengths, limitations, and applicability of different methods.
Exploration of dataset characteristics (diversity, scale, modality) and integration of real-world and synthetic data.

Main Results:

Identified three primary classes of face mask detection methods.
Emphasized the critical role of dataset quality and diversity in algorithmic performance.
Highlighted the benefits of multimodal data (depth, infrared) for real-world robustness.
Discussed the synergistic use of real and synthetic datasets to overcome limitations.
Examined emerging solutions like lightweight models, domain adaptation, and privacy-preserving techniques.

Conclusions:

Accurate and robust face mask detection systems are essential for public health.
Dataset quality, diversity, and multimodal integration are key to improving detection capabilities.
Emerging techniques offer pathways to enhance algorithmic efficiency, scalability, and privacy.