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Attendance Management System with Half-Covered and Full-Facial Recognition Feature

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Proceedings of the 9th International Conference on Computational Science and Technology (ICCST 2022)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 983))

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Abstract

Manual way of tracking attendance is inefficient and has vulnerability in protecting personal data. Various attendance management systems have been introduced to replace the manual attendance tracking process. This includes the integration of face recognition technique in the attendance management system. However, following the outbreak of pandemic COVID-19, we are strongly advised to always put on a face mask to protect ourselves and others. This practice has caused problems to existing attendance management system with facial recognition. This is because the mask has covered the essential data that can be measured and extracted by the facial recognition algorithms. To overcome this problem, an attendance management system with half-covered and full-facial recognition feature is proposed. MobileFaceNet model is used to verify the user identity for a real-time attendance check-in. Users are able to take attendance via the application with or without a face mask.

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Author information

Authors and Affiliations

  1. Multimedia University, Melaka, Malaysia

    Kah-Ong Goh, Check-Yee Law, Cu-Kang Tin & Connie Tee

  2. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia

    Yong-Wee Sek

Authors
  1. Kah-Ong Goh
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  2. Check-Yee Law
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  3. Cu-Kang Tin
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  4. Connie Tee
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  5. Yong-Wee Sek
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Corresponding author

Correspondence to Check-Yee Law .

Editor information

Editors and Affiliations

  1. Dongseo University, Busan, Korea (Republic of)

    Dae-Ki Kang

  2. Faculty of Computing and Informatics, Universiti of Malaysia Sabah, Kota Kinabalu, Malaysia

    Rayner Alfred

  3. Higher Colleges of Technology, Abu Dhabi, United Arab Emirates

    Zamhar Iswandono Bin Awang Ismail

  4. Universiti of Malaysia Sabah, Kota Kinabalu, Malaysia

    Aslina Baharum

  5. Asia Pacific University of Technology and Innovation, Kuala Lumpur, Malaysia

    Vinesh Thiruchelvam

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Goh, KO., Law, CY., Tin, CK., Tee, C., Sek, YW. (2023). Attendance Management System with Half-Covered and Full-Facial Recognition Feature. In: Kang, DK., Alfred, R., Ismail, Z.I.B.A., Baharum, A., Thiruchelvam, V. (eds) Proceedings of the 9th International Conference on Computational Science and Technology. ICCST 2022. Lecture Notes in Electrical Engineering, vol 983. Springer, Singapore. https://doi.org/10.1007/978-981-19-8406-8_18

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