On the effectiveness of local binary patterns in face anti-spoofing

By Ivana Chingovska, André Anjos, Sébastien Marcel. 2012

1,048 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Zitong Yu
2. Yaojie Liu
3. Amin Jourabloo
4. Xiaoming Liu
5. Xiaogang Wang
6. Jianwei Yang
7. Hu Han
8. Shiguang Shan

Citations in the corpus

• Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “For anti-spoo fi ng performance assessment, ferrfake and ferrlive rates are computed, using the underlying LivDet 2013 Crossmatch dataset for the fi ngerprint modality, and the ReplayAttack database for face.”
• Anti-spoofing in Action: Joint Operation with a Verification System (2013, methodology): “The biometric community constantly arrives with new solutions addressing more and more challenging spoofing attacks [27], [30], [7], [28], some of which are successful against a variety of attacks [15].”
• Deep Face Recognition: A Survey (2018, unknown): “Replay-Attack [28] 2012 1300 50 – EER, HTER 0.”
• Deep Learning in the Wild (2018, background): “This might be facilitated by the available datasets: to develop and evaluate anti-spoofing tools, amongst others CASIA [66], MSU-USSA [43], and the Replay Attack Database [12] exist.”
• Face Spoofing Detection Using Colour Texture Analysis (2016, methodology): “To confirm the observations in Figure 3, we have also conducted a statistical analysis on the Replay-Attack Database [29].”
• Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks (2020, background): “Others take advantage of the differences between bonafide attempts and attacks through the use of texture [10], image quality measures [11] or frequency analysis [12].”
• Presentation Attack Detection for Face Recognition Using Light Field Camera (2015, methodology): “The proposed method I (c) indicates the [8] and [5].”
• LBP − T OP based countermeasure against face spoofing attacks (2012, methodology): “We used, as binary classifier, a linear projection derived from Linear Discriminant Analysis LDA as is [6].”
• Domain Generalization: A Survey (2021, background): “Medical Imaging DG is also critical to medical imaging where domain shift is often related to variations in clinical centers or patients [42], [121].”
• 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].”
• Learn Convolutional Neural Network for Face Anti-Spoofing (2014, methodology): “• REPLAY-ATTACK database [7]: It also contains 50 subjects.”
• Deep Tree Learning for Zero-Shot Face Anti-Spoofing (2019, methodology): “Also, we test on previous face anti-spoofing databases, including CASIA [50], Replay-Attack [15], and MSU-MFSD [42]), compare with the state of the art.”
• Face Anti-Spoofing with Human Material Perception (2020, methodology): “Six databases including OULU-NPU [58], SiW [14], CASIA-MFSD [59], Replay-Attack [60], MSU-MFSD [61] and SiW-M [29] are used in our experiments.”
• Face anti-spoofing using patch and depth-based CNNs (2017, methodology): “For Replay-Attack, given its low image resolution, the patch size is 24× 24.”
• Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “Five databases - OULU-NPU [10, 5], SiW [34], CASIA-MFSD [56], Replay-Attack [12], DMAD are used in our experiment.”
• On the vulnerability of palm vein recognition to spoofing attacks (2015, background): “The main motivation of using printed images is based on that it is simple (easy to do), does not require prior knowledge about the system and it is already proved to be efficient in the context of other biometric modalities such as the finger vein [9], 2D face [1] or Iris [7].”
• Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect (2013, methodology): “On the contrary, it is concluded to be ineffectual in [6].”
• Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions (2014, methodology): “The full description of the database and its associated protocol can be found in [8].”