Face Spoofing Detection Using Colour Texture Analysis

By Zinelabinde Boulkenafet, Jukka Komulainen, Abdenour Hadid. IEEE Transactions on Information Forensics and Security, 2016

518 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/6ffe13f46bbd64ca2ba981bb57226251dfdc5f99

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In Signal Processing

Citations in the corpus

Face Presentation Attack with Latex Masks in Multispectral Videos (2017, background): “[3] for color texture analysis based face PAD.”
Face Anti-Spoofing: Model Matters, so Does Data (2019, background): “investigated different color spaces such as HSV and YCbCr [6, 8].”
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018, methodology): “To overcome the inﬂuence of illumination variation, they seek solutions in a different input domain, such as HSV and YCbCr color space [11,12], and Fourier spectrum [29].”
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, unknown): “9 Colour Texture [8] 30.”
Deep Anomaly Detection for Generalized Face Anti-Spoofing (2019, methodology): “For the second method, we choose [4], which consists in computing a color-based feature vector of high dimensionality (19998-length) by concatenating texture features based on Local Binary Patterns (LBPs) in two different color spaces ( i.e .”
Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning (2020, methodology): “We also use a number of conventional PAD methods as baselines, such as Multi-Scale LBP (MS LBP) [32], Binary CNN (CNN)[50], Image Distortion Analysis (IDA) [48], Color Texture (CT) [5] and LBPTOP [11].”
OULU-NPU: A Mobile Face Presentation Attack Database with Real-World Variations (2017, background): “The preliminary studies on generalized face spoof detection [2], [3], [6], [12], [13] have shown that these reported performances are indeed overly optimistic estimate on their actual performance in real-world authentication applications.”
Multi-Adversarial Discriminative Deep Domain Generalization for Face Presentation Attack Detection (2019, methodology): “Multi-scale LBP [19] and color textures [5] methods are proposed to extract various LBP descriptors in grayscale, RGB, HSV or YCbCr color spaces to differentiate real/fake faces.”
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations (2020, background): “In order to improve the robustness to light changes, some researchers have paid attention to different color spaces, such as HSV [3], YCbcR [2] and Fourier spectrum [18].”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, methodology): “In recent years, face PAD algorithms [26], [28] have achieved great performances.”
Single-Side Domain Generalization for Face Anti-Spoofing (2020, methodology): “This is because all other face anti-spoofing methods [6, 9, 23, 26, 39, 42] except for the MADDG [33] method pay no attention to the intrinsic distribution relationship among different domains.”
Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, background): “In recent years, several hand-crafted features based [7, 8, 15, 29, 45, 44] and deep learning based [49, 64, 36, 26, 62, 4, 19, 20] methods have been proposed for presentation attack detection (PAD).”
Face Anti-Spoofing with Human Material Perception (2020, unknown): “In the past decade, several hand-crafted feature based [2,3,4,5,6,7] and deep learning based [8,9,10,11] methods have been proposed for presentation attack detection (PAD).”
Face anti-spoofing using patch and depth-based CNNs (2017, background): “Researchers also seek solutions on different color spaces such as HSV and YCbCr [7, 8], Fourier spectra [28] and Optical Flow Maps (OFM) [5].”
Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018, background): “To overcome some of these difficulties, researchers tackle the problem in different domains, such as HSV and YCbCr color space [24,25], temporal domain [26,27,28,29] and Fourier spectrum [30].”
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals (2019, background): “Those are mostly based on ﬁnding inconsistencies in images, such as detecting distortions [10], ﬁnding compression artifacts [9], and assessing image quality [21].”