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Masked Facial Recognition in Security Systems Using Transfer Learning.

M Ramgopal¹, M Sai Roopesh¹, M Veeranna Chowdary¹

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Summary

This summary is machine-generated.

This study addresses the challenge of facial recognition with masks due to COVID-19. Researchers developed a solution using the YOLOv3 model to accurately identify individuals with masked faces, focusing on visible features like eyes and forehead.

Keywords:

Convolutional neural networks (CNN)Face recognition Machine learning (ML)Object detection model Transfer learning model

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Area of Science:

Computer Science
Artificial Intelligence
Biometrics

Background:

The COVID-19 pandemic necessitates widespread mask usage, significantly hindering traditional facial recognition systems.
Facial recognition is crucial for security and authentication in many organizations.
Existing facial recognition methods struggle with masked individuals, creating a need for specialized solutions.

Purpose of the Study:

To develop and evaluate a method for accurate facial recognition of individuals wearing face masks.
To adapt existing object detection models for the specific challenge of masked facial identification.

Main Methods:

Implementation of the YOLOv3 object detection model for identifying masked and unmasked faces.
Utilizing a dataset specifically curated for masked face recognition (Real-World Masked-Face-Data set).
Focusing on facial regions excluding the mask, primarily the eyes and forehead.

Main Results:

The YOLOv3 model demonstrated high performance with a detection time of 0.012 s.
Achieved an F1 score of 0.90 and a mean Average Precision (mAP) score of 0.92.
Experimental results confirmed the model's effectiveness on the Real-World Masked-Face-Data set.

Conclusions:

The proposed approach effectively handles facial recognition challenges posed by face masks.
The YOLOv3 model offers a rapid and efficient solution for identifying individuals in masked scenarios.
This research contributes to maintaining security and authentication functionalities during the COVID-19 pandemic.