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Mask - aware face recognition system
Dharanesh, Shreyas
Dharanesh, Shreyas
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Embargoed till June 2022
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2021-05
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Abstract
This work presents a novel approach to face recognition that recognizes faces both with
and without facial masks worn during the Covid-19 pandemic. The two-fold contribution of this
study are: evaluation of the hand-crafted descriptors for facial mask detection and deep learning
based method for face recognition in the presence of a mask. In this study, Local Binary Pattern
(LBP), Histogram of Oriented Gradients (HOG), Local Directional Order Pattern (LDOP), have
been used along with Linear Support Vector Machine (SVM) for facial mask detection and has
proven to result in a very high success rate of 99.6029 %. This study has classified the Face
Recognition system into two models: subject dependent (or closed-set) and subject independent
(or open-set).
Conventional solutions to face recognition use the entire facial image as the input to their
algorithms. This thesis presents an approach where hand-crafted feature descriptors are used to
identify the presence of a mask, If detected, switch to ocular recognition for those subjects wearing
mask else face recognition is performed. The ensemble method is incorporated which uses two
CNNs trained with face and ocular region of the identities where the system dynamically switches
between the CNNs when the mask is detected in the input image. Open-set and closed-set
approaches followed in experiments are conducted on the Real-World Masked Face Recognition
Dataset (RMFRD) the database of masked and unmasked faces. Finally, the analysis shows how
the granularity of this experiment can be leveraged to obtain an improved accuracy rate over the
traditional full-face recognition approach in the presence of a face covering.
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Thesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Electrical Engineering and Computer Science
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Wichita State University
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© Copyright 2021 by Shreyas Dharanesh
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