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An Improved Face Mask-aware Recognition System Based on Deep Learning
International Conference on Data Science, Computation, and Security, IDSCS 2022 ; 462:15-29, 2022.
Article in English | Scopus | ID: covidwho-1971615
ABSTRACT
Face mask detection and recognition have been incorporated into many applications in daily life, especially during the current COVID-19 pandemic. To mitigate the spread of coronavirus, wearing face masks has become commonplace. However, traditional face detection and recognition systems utilize main facial features such as the mouth, nose, and eyes to determine a person’s identity. Masks make facial detection and recognition tasks more challenging since certain parts of the face are concealed. Yet, how to improve the performance of existing systems with a face mask overlaid on the original face input images remains an open area of inquiry. In this study, we propose an improved face mask-aware recognition system named ‘MAR’ based on deep learning, which can tackle challenges in face mask detection and recognition. MAR consists of five main modules to handle various kinds of input images. We re-train the CenterNet model with our augmented face mask inputs to perform face mask detection and propose four variations on face mask recognition models based on the pre-trained ArcFace to handle facial recognition. Finally, we demonstrate the effectiveness of our proposed models on the VGGFACE2 dataset and achieve a high accuracy score on both detection and recognition tasks. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: International Conference on Data Science, Computation, and Security, IDSCS 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: International Conference on Data Science, Computation, and Security, IDSCS 2022 Year: 2022 Document Type: Article