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Face Spoofing Detection Using DenseNet

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Intelligent Human Computer Interaction (IHCI 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12616))

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Abstract

Today, face recognition is the most widely used biometric recognition technology. However, face recognition is a biometric method that is vulnerable to spoofing. Types of spoofing attack include print, replay, and 3D mask. Methods based on hand-crafted features such as local binary patterns which use high-frequency features of images, are therefore vulnerable to blur caused by optical factors or motion. Recently, face spoofing detection methods based on learned features using the convolutional neural network series have been introduced. Among them, DenseNet-121 has a densely connected structure unlike the other structure, so it can widely reflect the characteristics of various frequency bands of an image. In this paper, we study face-spoofing detection using DenseNet-121. For the performance measurement, CASIA-FASD and a lab-made PR-FSAD were used. As a result of the experiment, it was confirmed that DenseNet showed good face spoofing detection performance in both DBs. This result can be analyzed because of the structural characteristics that DenseNet-121 well reflects the wide frequency characteristics of the image.

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Acknowledgement

This research was supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean government, MSIT(NRF-2016M3A9E1915855). Also, this work was financially supported by a Grant (2018000210004) from the Ministry of Environment, Republic of Korea.

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Authors and Affiliations

  1. Department of Artificial Intelligence and Informatics, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul, 03016, Republic of Korea

    Su-Gyeong Yu & So-Eui kim

  2. Department of Computer Science, Graduate School, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul, 03016, Republic of Korea

    Kun Ha Suh

  3. Department of Human-Centered Artificial Intelligence, Sangmyung University, Hongjimun 2-Gil 20, Jongno-Gu, Seoul, 03016, Republic of Korea

    Eui Chul Lee

Authors
  1. Su-Gyeong Yu
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  2. So-Eui kim
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  3. Kun Ha Suh
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  4. Eui Chul Lee
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Corresponding author

Correspondence to Eui Chul Lee .

Editor information

Editors and Affiliations

  1. Woosong University, Daejeon, Korea (Republic of)

    Madhusudan Singh

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

    Dae-Ki Kang

  3. Keimyung University, Daegu, Korea (Republic of)

    Jong-Ha Lee

  4. Indian Institute of Information Technology, Allahabad, India

    Uma Shanker Tiwary

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

    Dhananjay Singh

  6. Pukyong National University, Busan, Korea (Republic of)

    Wan-Young Chung

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Yu, SG., kim, SE., Suh, K.H., Lee, E.C. (2021). Face Spoofing Detection Using DenseNet. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12616. Springer, Cham. https://doi.org/10.1007/978-3-030-68452-5_24

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  • DOI: https://doi.org/10.1007/978-3-030-68452-5_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68451-8

  • Online ISBN: 978-3-030-68452-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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