Fingerprint liveness detection by local phase quantization

By Luca Ghiani, Gian Luca Marcialis, Fabio Roli. 2012

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Citations in the corpus

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, methodology): “Among the most common techniques for static (extracted from single image) texture-based anti-spoofing methods are statistical features [19], Power Spectrum Fourier analysis [20], Ridge Frequency Analysis [19], Local Binary Patterns (LBP) [21] and Local Phase Quantisation [22].”
Automatic adaptation of fingerprint liveness detector to new spoof materials (2014, methodology): “Local Phase Quantization (LPQ) [7] utilizes phase information computed locally in a window.”
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection (2014, methodology): “Ghiani et al. [69] explored LPQ [46], a method for representing all spectrum characteristics in a compact feature representation form.”
Toward an attack-sensitive tamper-resistant biometric recognition with a symmetric matcher: A fingerprint case study (2014, methodology): “such as local phase quantisation [15] and binarized Statistical Image Feature [16] could have been used.”
Fingerprint Liveness Detection Using Convolutional Neural Networks (2016, methodology): “In contrast to more sophisticated techniques that use texture descriptors as features vectors, such as Local Phase Quantization (LPQ) [15], LBP with wavelets [16], and BSIF [17], their LBP implementation uses the original and uniform LBP coding schemes.”
A Survey on Anti-Spooﬁng Schemes for Fingerprint Recognition Systems (2014, methodology): “A novel set of features for detecting liveness based on the LPQ of the ﬁngerprint image were deﬁned by Ghiani et al. [Ghiani et al. 2012b] [Ojansivu et al. 2008] [Ojansivu and Heikkil¨a 2008] [Martins et al. 2012].”