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- W3208637248 endingPage "2666" @default.
- W3208637248 startingPage "2666" @default.
- W3208637248 abstract "A large number of intelligent models for masked face recognition (MFR) has been recently presented and applied in various fields, such as masked face tracking for people safety or secure authentication. Exceptional hazards such as pandemics and frauds have noticeably accelerated the abundance of relevant algorithm creation and sharing, which has introduced new challenges. Therefore, recognizing and authenticating people wearing masks will be a long-established research area, and more efficient methods are needed for real-time MFR. Machine learning has made progress in MFR and has significantly facilitated the intelligent process of detecting and authenticating persons with occluded faces. This survey organizes and reviews the recent works developed for MFR based on deep learning techniques, providing insights and thorough discussion on the development pipeline of MFR systems. State-of-the-art techniques are introduced according to the characteristics of deep network architectures and deep feature extraction strategies. The common benchmarking datasets and evaluation metrics used in the field of MFR are also discussed. Many challenges and promising research directions are highlighted. This comprehensive study considers a wide variety of recent approaches and achievements, aiming to shape a global view of the field of MFR." @default.
- W3208637248 created "2021-11-08" @default.
- W3208637248 creator A5004700474 @default.
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- W3208637248 creator A5006070879 @default.
- W3208637248 creator A5057875301 @default.
- W3208637248 creator A5064284704 @default.
- W3208637248 date "2021-10-31" @default.
- W3208637248 modified "2023-10-10" @default.
- W3208637248 title "Masked Face Recognition Using Deep Learning: A Review" @default.
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- W3208637248 doi "https://doi.org/10.3390/electronics10212666" @default.
- W3208637248 hasPublicationYear "2021" @default.
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