Automated and contactless face recognition is a widely used machine learning technology for identifying people which has been applied in scenarios like secure login to electronic devices, automated border control, community surveillance, tracking school attendance. The use of face masks has become essential due to the global spread of COVID-19, raising concerns about the performance of recognition systems. Conventional face recognition technologies were primarily designed to work with unmasked faces, and the widespread use of masked face images significantly degrades their performance. To address this understudied issue, we evaluated the performance of six deep learning models, namely, VGG-16, AlexNet, GoogleNet, LeNet, ResNet-50, and FaceNet on masked and unmasked face images. We aim to find out if deep learning models struggle with masked face recognition and identify the models that mitigate the impact of masked face images. We track, and report miss rates for both masked and unmasked images, along with performance metrics like accuracy and F1 scores in this paper.
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An Exploratory Study of Masked Face Recognition with Machine Learning Algorithms
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.
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- Award ID(s):
- 1900087
- PAR ID:
- 10466343
- Date Published:
- Journal Name:
- IEEE SoutheastCon 2023
- Page Range / eLocation ID:
- 877 to 882
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Over the past two years, face masks have been a critical tool for preventing the spread of COVID-19. While previous studies have examined the effects of masks on speech recognition, much of this work was conducted early in the pandemic. Given that human listeners are able to adapt to a wide variety of novel contexts in speech perception, an open question concerns the extent to which listeners have adapted to masked speech during the pandemic. In order to evaluate this, we replicated Toscano and Toscano (PLOS ONE 16(2):e0246842, 2021), looking at the effects of several types of face masks on speech recognition in different levels of multi-talker babble noise. We also examined the effects of listeners’ self-reported frequency of encounters with masked speech and the effects of the implementation of public mask mandates on speech recognition. Overall, we found that listeners’ performance in the current experiment (with data collected in 2021) was similar to that of listeners in Toscano and Toscano (with data collected in 2020) and that performance did not differ based on mask experience. These findings suggest that listeners may have already adapted to masked speech by the time data were collected in 2020, are unable to adapt to masked speech, require additional context to be able to adapt, or that talkers also changed their productions over time. Implications for theories of perceptual learning in speech are discussed.
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/SoutheastCon51012.2023.10115205
