Abstract
Fake fingerprint detection technology is used for detecting spoof fingerprint attacks in biometric systems. In this paper, an improved software-based fake fingerprint detection approach using wavelet analysis and local binary pattern(LBP) is proposed. Firstly, wavelet analysis is applied to get the denoised image and residual noise image. Then both two images are divided into blocks of the same size to calculate the histogram of LBP as features, which provide more texture information than the features in original wavelet-based method. Finally, support vector machine(SVM) is used for classification. The average rate of accuracy of the proposed approach is 88.53% for all datasets in LivDet2011, and 88.98% in LiveDet2013, while the winner in LivDet2011 is 74.41%, and the winner in LivDet2013 is 86.63%.
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Zhang, Y., Fang, S., Xie, Y., Xu, T. (2014). Fake Fingerprint Detection Based on Wavelet Analysis and Local Binary Pattern. 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_21
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DOI: https://doi.org/10.1007/978-3-319-12484-1_21
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