LBP − T OP based countermeasure against face spooﬁng attacks

By Tiago de Freitas Pereira, André Anjos, José Mario De Martino, Sébastien Marcel. 2012

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Citations in the corpus

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, methodology): “The implementation of the face spoofing-detection algorithm based on the open source package provided from the original work [26] yielded an accuracy of 85% on ReplayAttack.”
Presentation Attack Detection Methods for Face Recognition Systems (2017, methodology): “Early work in this direction is based on Local Binary Patterns from three orthogonal planes (LBP-TOP) [De Feitas Pereira et al. 2013] and has demonstrated a reasonable performance on the replay attack database [Chingovska et al. 2012].”
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “One is training on the CASIA-MFSD and testing on Replay-Attack, which we name protocol CR; the other is training on the Replay-Attack and testing on CASIA-7 50.2 47.9 LBP-1 [15] 55.9 57.6 LBP-TOP [15] 49.7 60.6 Motion-Mag [3] 50.1 47.0 Spectral cubes [44] 34.4 50.0 CNN [51] 48.5 45.5 LBP-2 [7] 47.0 ”
Face Anti-Spoofing: Model Matters, so Does Data (2019, methodology): “For example, many hand-crafted features have been studied in previous works, including LBP [29, 12, 13], HOG [21, 43], DOG [37, 33], SIFT [32], and SURF [9].”
Biometrics Evaluation Under Spoofing Attacks (2014, methodology): “The ﬁrst one uses (LBP) [34], 7 while the second one an LBP variant capturing dynamic texture properties in three orthogonal planes (LBP-TOP) [44].”
LBP with Six Intersection Points: Reducing Redundant Information in LBP-TOP for Micro-expression Recognition (2014, background): “In dealing with dynamic textures that evolve over space-time dimensions, the LBP-TOP remains a popular choice of feature extraction for various applications such as texture recognition [13], face spoofing [1], gait and action recognition [3, 5], and facial expression recognition [14, 15].”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, methodology): “, fixed pattern noise and the noise resulting from the photo-responsiveness of nonuniform light-sensitive cells) [142]; using modified versions of the popular Local Binary Patterns (LBP), in order to take into account temporal information present in video sequences [125] or to analyse the dynamics o”
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018, methodology): “Many hand-crafted feature-based methods may improve intra-database testing performance by simply concatenating the features of consecutive frames to train the classiﬁers [11,18,28].”
Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, methodology): “Several classical lo-cal descriptors such as LBP [7, 15], SIFT [44], SURF [9], HOG [29] and DoG [45] are utilized to extract frame level features while video level methods usually capture dynamic clues like dynamic texture [28], micro-motion [53] and eye blinking [41].”
Anti-spoofing in Action: Joint Operation with a Verification System (2013, methodology): “The second algorithm [9] uses an LBP variant which extends the operator into temporal dimension, thus capturing dynamic texture properties in three orthogonal planes (LBP-TOP) [29].”
Face anti-spoofing using patch and depth-based CNNs (2017, methodology): “Moreover, we also explore several other input feature maps to the CNN including a pixel-wise LBP map, and high-frequency patches.”
Face Anti-Spoofing with Human Material Perception (2020, methodology): “Several classical lo-cal descriptors such as LBP [2,4], SIFT [7], SURF [19], HOG [5] and DoG [6] are utilized to extract frame level features while video level methods usually capture dynamic cues like dynamic texture [20], micro–motion [21] and eye blinking [22].”
Face Spoof Detection With Image Distortion Analysis (2015, methodology): “A study reported in [17] showed that for two of the texture based methods (proposed in [4] and [16]), the HTER increased dramatically under the cross-database scenarios (where the training and testing sets came from different face spoof databases).”
Face Recognition on Consumer Devices: Reflections on Replay Attacks (2015, background): “There has been a significant focus on solving the problem of presenting fake biometrics (spoofing attacks) to sensors [7]–[9], [11], [13], [33], [34].”
Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018, methodology): “Early works apply the hand-crafted feature descriptors, such as LBP [3,4,21], HoG [5,6], SIFT [22] and SURF [23], to project the faces to a low-dimension embedding.”
Learn Convolutional Neural Network for Face Anti-Spoofing (2014, background): “, LBP [23], LBP-TOP [11] are still unable to capture the most discriminative cues between genuine and fake faces.”