Abstract
Aiming to counterstrike the spoofing attacks in face recognition system, a non-intrusive face spoofing detection method based on guided filtering and image quality analysis is proposed. Guided image filtering (GIF) is first implemented for the enhancement of texture component of facial image, and then the local texture features are extracted by calculating local binary patterns (LBP). Meanwhile, the global facial image quality features are obtained from image quality measures. With these features, the spoofing detection is accomplished by using support vector machine (SVM) classifier. Experiments results indicate its effectiveness and it has great potential to be applied for the authenticity verification in face recognition system.
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Acknowledgements
This work was supported in part by project supported by National Natural Science Foundation of China (Grant No. 61572182, 61370225), project supported by Hunan Provincial Natural Science Foundation of China (Grant No. 15JJ2007), and supported by the Scientific Research Plan of Hunan Provincial Science and Technology Department of China (2014FJ4161). The authors would like to thank the Idiap and CASIA institutes for sharing their face spoofing databases.
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Peng, F., Qin, L., Long, M. (2017). POSTER: Non-intrusive Face Spoofing Detection Based on Guided Filtering and Image Quality Analysis. In: Deng, R., Weng, J., Ren, K., Yegneswaran, V. (eds) Security and Privacy in Communication Networks. SecureComm 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 198. Springer, Cham. https://doi.org/10.1007/978-3-319-59608-2_49
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DOI: https://doi.org/10.1007/978-3-319-59608-2_49
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