An Exploratory Study of Masked Face Recognition with Machine Learning
  Algorithms
Megh Pudyel Mustafa Atay
Automated face recognition is a widely adopted machine learning technology
for contactless identification of people in various processes such as automated
border control, secure login to electronic devices, community surveillance,
tracking school attendance, workplace clock in and clock out. Using face masks
have become crucial in our daily life with the recent world-wide COVID-19
pandemic. The use of face masks causes the performance of conventional face
recognition technologies to degrade considerably. The effect of mask-wearing in
face recognition is yet an understudied issue. In this paper, we address this
issue by evaluating the performance of a number of face recognition models
which are tested by identifying masked and unmasked face images. We use six
conventional machine learning algorithms, which are SVC, KNN, LDA, DT, LR and
NB, to find out the ones which perform best, besides the ones which poorly
perform, in the presence of masked face images. Local Binary Pattern (LBP) is
utilized as the feature extraction operator. We generated and used synthesized
masked face images. We prepared unmasked, masked, and half-masked training
datasets and evaluated the face recognition performance against both masked and
unmasked images to present a broad view of this crucial problem. We believe
that our study is unique in elaborating the mask-aware facial recognition with
almost all possible scenarios including half_masked-to-masked and
half_masked-to-unmasked besides evaluating a larger number of conventional
machine learning algorithms compared the other studies in the literature.