Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect

By Nesli Erdogmus, Sébastien Marcel. 2013

310 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Safety, Robustness & Interpretability, Signal Processing

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Citations in the corpus

Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning (2017, background): “There have been some attacks for non deep neural network based face recognition systems as well [24], [13], [37], [28], [23].”
Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition (2016, background): “These attacks are not inconspicuous, and can largely be thwarted by anti-spoofing mechanisms, such as liveness detection [7, 22].”
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems (2016, methodology): “Matching score distributions of fake fingerprints (top row) and faces (bottom row) for LivDet09-Silicone (Sensor : Biometrika, Matcher : Veryfinger), LivDet11-Alginate (S: Biometrika, M: Bozorth3), LivDet11-Gelatin (S: Italdata, M: Bozorth3), LivDet11-Latex (S: Italdata, M: Bozorth3), Personal Photo”
Detection and Continual Learning of Novel Face Presentation Attacks (2021, neutral): “Pavlidis Symosek [7] 2000 − Facial disguises 3DMAD [5] 2013 17 3D mask attacks I(2)BVSD [4] 2013 75 Facial disguises GUC-LiFFAD [8] 2015 80 2D print and replay MS-Spoof [3] 2015 21 2D print BRSU [11] 2016 50 3D masks EMSPAD [9] 2017 50 2D print MLFP [1] 2017 10 2D & 3D masks CASIA-SURF [12] 2020 100”
RGB-D datasets using microsoft kinect or similar sensors: a survey (2016, methodology): “The curves under the images are the accelerometer data at 50 Hz of devices attached to the knife, the mixing spoon, the small spoon, the peeler, the glass, the oil bottle, and the pepper dispenser mentioned datasets above, another 26 extra RGB-D datasets for different applications are also added int”
ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes (2017, background): “A large number of RGB-D datasets have been captured and made publicly available for training and benchmarking [56, 34, 50, 65, 79, 83, 74, 4, 58, 81, 15, 55, 1, 68, 30, 51, 21, 48, 43, 92, 80, 61, 72, 93, 36, 16, 35, 57, 40, 29, 70, 52, 45, 95, 75, 9, 33, 85, 71, 32, 3, 10, 78, 2].”
Face Spoofing Detection Using Colour Texture Analysis (2016, background): “Although the number of publicly available datasets is still quite scarce, new anti-spoofing databases appear gradually due to the increasing interest in anti-spoofing by the research community [3], [4], [6], [29], [30] and international competitions [31], [32].”
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection (2014, methodology): “Finally, Section VI concludes the paper and discusses some possible future directions.”
Deep Anomaly Detection for Generalized Face Anti-Spoofing (2019, background): “masks - 3DMAD [13], HKBU [22], CSMAD [3]); ii) others focus on the study of different image sources (depth/NIR/thermal) such as CASIA-SURF [37] or CSMAD; iii) others attempt to simulate a certain scenario like a mobile device setting, where the user hold the device (e.”
Face Anti-spoofing: Visual Approach (2014, background): “The first public dataset for 2D face antispoofing to contain 3D mask attacks is that of Erdogmus and Marcel [22].”
Presentation Attack Detection Methods for Face Recognition Systems (2017, background): “…(ISV) 92.6 [Chingovska et al. 2014] [Nesli and Marcel 2013] Intersession Variability (ISV) 65.7 [Nesli and Marcel 2013] Gabor graph 78.3 IDIAP-Print Attack DB [Erdogmus and Marcel 2013] [Anjos and Marcel 2011] Log-Gabor Binary Pattern Histogram (LGBPHS) 97.5 [Erdogmus and Marcel 2013] Fig.”
Face Presentation Attack with Latex Masks in Multispectral Videos (2017, background): “of Samples 2D Masks 3D Masks (Hard/Latex/ Silicone) Varying Acquisition Environment Videos Multispectral (Wavelength in nm) 3DMAD (2013) [8] 17 255 × Hard × X ×”
Face Spoof Detection With Image Distortion Analysis (2015, background): “A genuine face image (a) of a subject in the Idiap databases [4], [5] and three examples of spoofs of the same subject using a (b) printed photo, (c) displayed photo (on a tablet screen), and (d) 3D face mask.”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, methodology): “These different attack versions may be found in the face spoofing databases available for research [54], [59], [102], [105], [107], [108], [111], which are reviewed in Sect.”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, background): “3DMAD [10] 2013 17 255 VIS/Kinect RGB/Depth 3D Mask”
Spoofing Face Recognition With 3D Masks (2014, methodology): “The authors would like to express their thanks to the FP7 European TABULA RASA project for the financial support and to the partners of the consortium for their collaboration; particularly to Morpho for giving access to their database and to Eurecom for cooperating in reproduction of their results.”