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Occluded Face Recognition with Deep Learning

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Computing and Data Science (CONF-CDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1513))

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Abstract

Face recognition is an research topic of great importance in both the academia and industry, with a large number of applications based on this technology. Occluded face recognition is even more challenging when the whole face is not available. We find this problem has not been completely solved and is still a hot topic in recent years. Based on a real-world masked face dataset, we conduct a series of experiments with an aim of evaluating five advanced deep learning models and find that DenseNet performs the best with a test accuracy of 0.8012.

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Author information

Authors and Affiliations

  1. School of Information Science and Technology, Fudan University, Shanghai, China

    Qin Jiayu

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  1. Qin Jiayu
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Correspondence to Qin Jiayu .

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Editors and Affiliations

  1. Chinese Academy of Sciences, Beijing, China

    Weijia Cao

  2. University of California, Los Angeles, CA, USA

    Aydogan Ozcan

  3. China Academy of Space Technology, Beijing, China

    Haidong Xie

  4. Chinese Academy of Sciences, Beijing, China

    Bei Guan

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Jiayu, Q. (2021). Occluded Face Recognition with Deep Learning. In: Cao, W., Ozcan, A., Xie, H., Guan, B. (eds) Computing and Data Science. CONF-CDS 2021. Communications in Computer and Information Science, vol 1513. Springer, Singapore. https://doi.org/10.1007/978-981-16-8885-0_3

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  • DOI: https://doi.org/10.1007/978-981-16-8885-0_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8884-3

  • Online ISBN: 978-981-16-8885-0

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