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IJSTR >> Volume 9 - Issue 10, October 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



A Comparative Analysis Of Face Recognition Models On Masked Faces

[Full Text]

 

AUTHOR(S)

Yalavarthi Bharat Chandra, Gouru Karthikeya Reddy

 

KEYWORDS

 

ABSTRACT

Face recognition systems are among the widely used biometrics in fields such as surveillance, access controls, attendance, forensics and other security purposes. Due to current Covid-19 crisis almost everyone can be seen wearing a mask in public. This change can be very challenging for existing facial recognition systems and can make them less effective. A face mask covers significant portion of the face making facial recognition systems having less face features to recognize and on top of that face masks can also add significant noise to the image. As wearing face masks is going to be new normal it is important to understand and study how current state of art face recognition models perform in recognizing masked faces. We did a comparative analysis on four state of art deep learning models which are widely used in this field 1)VGGFace 2)FaceNet 3)OpenFace 4)DeepFace. The analysis is made on face verification task on RMFRD dataset which is largest real world masked face dataset available. We compared the models on various metrics like error rate, accuracy ,precision, verification time.

 

REFERENCES

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