Power spectrum-based fingerprint vitality detection

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How researchers see this paper

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Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, methodology): “statistical features [19], Power Spectrum Fourier analysis [20], Ridge Frequency Analysis [19], Local Binary Patterns (LBP) [21] and Local Phase Quantization [22].”
Fingerprint liveness detection by local phase quantization (2012, methodology): “Each algorithm is based on a different principle with respect to ours, expect for [3].”
Fake-fingerprint detection using multiple static features (2009, background): “30,33 Even though the static approach is useful in practice, it falls short of the dynamic approach in that it has to make a decision based only a single image.”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, methodology): “For spoofs, most of the energy in the Fourier domain is present in the center of the image which corresponds to low frequency data in the image domain [Coli et al. 2007a].”
Semi-supervised stacked autoencoder-based deep hierarchical semantic feature for real-time fingerprint liveness detection (2019, methodology): “Coli [11] proposed a novel detection method that catego-rizes spoof fingerprints after the Fourier transformations of fingerprint images by using the minutiae points of live fingerprints, which are not easily visible in fake fingerprints because of inconsistency of ridges and the rough surface.”