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Masked Face Recognition Model with Explainable AI

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Intelligent Human Computer Interaction (IHCI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13741))

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Abstract

Due to the recent COVID-19 pandemic, people tend to wear masks indoors and outdoors. Therefore, systems with face recognition, such as FaceID, showed a tendency of decline in accuracy. Consequently, many studies and research were held to improve the accuracy of the recognition system between masked faces. Most of them targeted to enhance dataset and restrained the models to get reasonable accuracies. However, not much research was held to explain the reasons for the enhancement of the accuracy. Therefore, we focused on finding an explainable reason for the improvement of the model’s accuracy. First, we could see that the accuracy has actually increased after training with a masked dataset by 12.86%. Then we applied Explainable AI (XAI) to see whether the model has really focused on the regions of interest. Our approach showed through the generated heatmaps that difference in the data of the training models make difference in range of focus.

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Acknowledgement

This work was supported by the Ministry of Trade, Industry & Energy (MI, Korea) (P0019323).

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Authors and Affiliations

  1. Department of AI & Informatics, Graduate School, Sangmyung University, Seoul, Republic of Korea

    Hyeon Ah Sung & Seunghyun Kim

  2. Department of Human-Centered Artificial Intelligence, Sangmyung University, Seoul, Republic of Korea

    Eui Chul Lee

Authors
  1. Hyeon Ah Sung
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  2. Seunghyun Kim
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  3. Eui Chul Lee
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Corresponding author

Correspondence to Eui Chul Lee .

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Editors and Affiliations

  1. Tashkent University Information Technologies, Tashkent, Uzbekistan

    Hakimjon Zaynidinov

  2. Oregon Institute of Technology, Klamath Falls, USA

    Madhusudan Singh

  3. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

  4. Hankuk University of Foreign Studies, Yongin, Korea (Republic of)

    Dhananjay Singh

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Sung, H.A., Kim, S., Lee, E.C. (2023). Masked Face Recognition Model with Explainable AI. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-27199-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-27198-4

  • Online ISBN: 978-3-031-27199-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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