Abstract
With the development of E-Commerce, biometric based on-line authentication is more competitive and is paid more attentions. It brings about one of hot issues of liveness detection recently. In this paper, we propose a liveness detection scheme to combine Fourier statistics and local binary pattern (LBP). First, The Gamma correction and DoG filtering are utilized to reduce the illumination variation and to preserve the key information of the image. Then the Fourier statistics and LBP are combined together to form a new feature vector. Finally, a SVM classifier is trained to discriminate the live and forge face image. The experimental results on the NUAA demonstrate that the proposed scheme is efficient and robust.
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Wu, L., Xu, X., Cao, Y., Hou, Y., Qi, W. (2014). Live Face Detection by Combining the Fourier Statistics and LBP. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_19
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DOI: https://doi.org/10.1007/978-3-319-12484-1_19
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12483-4
Online ISBN: 978-3-319-12484-1
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