Crafting A Panoptic Face Presentation Attack Detector
Citations
109 citations
Cites methods from "Crafting A Panoptic Face Presentati..."
...Other methods have been developed that use the available CNN architectures with customized components and were trained on spoofing databases [40, 41, 15, 42, 43]....
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77 citations
Cites methods from "Crafting A Panoptic Face Presentati..."
...[27] trained an Alexnet model with a combination of cross-entropy and focal losses....
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53 citations
Cites background from "Crafting A Panoptic Face Presentati..."
...Given the vulnerabilities, it is our belief that future research should focus primarily on developing (i) robust PAD algorithms and (ii) universal detectors (Mehta et al. 2019) capable of handling multiple attacks....
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36 citations
Cites methods from "Crafting A Panoptic Face Presentati..."
...Existing methods tried to explore the difference between the real face skin and 3D fake face materials based on the reflectance properties using multispectral imaging [7], [8], texture analysis [9], [10], deep features [11], [12], or liveness cues [13], [14]....
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10 citations
References
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"Crafting A Panoptic Face Presentati..." refers methods in this paper
...To train the proposed presentation attack detector, we use the AlexNet architecture with cyclic learning rates [27] and stochastic gradient descent (SGD) optimization with warm restarts and differential learning rates across the layers....
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680 citations
"Crafting A Panoptic Face Presentati..." refers background or methods in this paper
...[30] is a standard physical attack database consisting of 600 video samples from the warped photo, cut photo, and video attacks in three qualities: low, normal, and high....
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...Majority of the existing anti-spoof methods involve extraction of discriminating features to analyze the face texture, such as Haralick texture features, local binary pattern (LBP), partial least square (PLS), and difference of Gaussian (DoG) [1, 2, 3, 20, 30]....
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611 citations
"Crafting A Panoptic Face Presentati..." refers background in this paper
...Dynamic anti-spoofing techniques mostly target blinking [13, 18], motion magnification [6], or liveness detection [8, 28], given a sequence of frames....
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