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Multi-stream CNN for Face Anti-spoofing Using Color Space Analysis

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Part of the Communications in Computer and Information Science book series (CCIS,volume 1376)

Abstract

Face recognition systems are one of the fastest, accurate and most accessible biometric modalities. These systems are widely used in a variety of applications such as ID verification in phones, surveillance, border control and security checks in payment methods. However, these face recognition systems are prone to major security threats due to different types of spoof attacks (presentation attacks). To address this issue, we propose a multi-stream CNN based architecture for analyzing different color spaces of face images. Different color spaces help us to discriminate between real and spoof images. In order to consider local information, we consider analyzing patches instead of the entire image. We evaluate our architecture on different benchmark databases such as CASIA-FASD, MSU-USSA, and REPLAY-ATTACK to see its efficiency as compared to other approaches.

Keywords

  • Face anti-spoofing
  • Biometric authentication
  • Presentation attacks
  • Multi-stream CNN
  • Deep learning

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Correspondence to Purva Mhasakar .

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Mhasakar, P., Mandal, S., Mitra, S.K. (2021). Multi-stream CNN for Face Anti-spoofing Using Color Space Analysis. In: Singh, S.K., Roy, P., Raman, B., Nagabhushan, P. (eds) Computer Vision and Image Processing. CVIP 2020. Communications in Computer and Information Science, vol 1376. Springer, Singapore. https://doi.org/10.1007/978-981-16-1086-8_18

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  • DOI: https://doi.org/10.1007/978-981-16-1086-8_18

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  • Print ISBN: 978-981-16-1085-1

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