Face Liveness Detection from a Single Image with Sparse Low Rank Bilinear Discriminative Model

By Xiaoyang Tan, Yi Li, Jun Liu, Lin Jiang. 2010

578 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “The first category assessing textural properties is the more widespread group with approaches like LBP [24], or statistical features [25] exploiting the observation that images/videos with spoofed faces (printed or replayed) do not exhibit the same noise-level like genuine samples.”
Face liveness detection with component dependent descriptor (2013, methodology): “The ﬁrst row is NUAA database [24], the second is PRINT-ATTACK database [1], and the third is CASIA database [25]. ble to inaccuracy detection or tracking on face components.”
Face spoofing detection from single images using micro-texture analysis (2011, methodology): “For performance evaluation, we considered the publicly available NUAA Photograph Imposter Database [12] which contains images of both real client accesses and photo at tacks.”
Face Anti-Spoofing: Model Matters, so Does Data (2019, methodology): “Besides, existing datasets for face antispoofing including NUAA [37], CASIA-MFSD [44], and Replay-Attack [11] were released years ago.”
Motion-based counter-measures to photo attacks in face recognition (2014, background): “Database [11], which contains only single-shot photo attacks and a poorly defined protocol for training, tunning and testing of counter-measures.”
Face Spoof Detection With Image Distortion Analysis (2015, methodology): “In this section, we provide a brief summary of three public-domain face spoof databases: NUAA Photograph Imposter database [8], Idiap REPLAY-ATTACK database [4] and CASIA Face Anti-Spoofing Database [9].”
Presentation Attack Detection Methods for Face Recognition Systems (2017, methodology): “…subsystems and presentation attack detection methods in face recognition systems [Hadid 2014; Galbally et al. 2014a], publicly available databases [Tan et al. 2010; Anjos and Marcel 2011; Chingovska et al. 2012; Zhang et al. 2012], dedicated books [Marcel et al. 2014], patents [Troy et al. 2014;…”
Face Anti-Spoofing with Human Material Perception (2020, background): “h dierence between live and spoong faces [14,16] can be adopted as signicant cue as most spoong faces are broadcasted in plane presentation attack instruments (PAIs). Thirdly, reectance dierence [17,18] is also one kind of reliable cues as human facial skin and spoong surfaces react dierently to cha”
Counter-measures to photo attacks in face recognition: A public database and a baseline (2011, background): “For example, [10] explores the Lambertian reflectance model to derive differences between the 2-D images of the face presented during an attack and a real (3-D) face, in real-access attempts.”
Face liveness detection by learning multispectral reflectance distributions (2011, background): “It is also noticed that some researchers attempt to detect liveness from a single image [9], [10].”
A face antispoofing database with diverse attacks (2012, methodology): “Another shortcoming in variation is the quality of attacks: in [5, 2, 6], algorithms extract the high frequency information (e.”
Deep Representations for Iris, Face, and Fingerprint Spoofing Detection (2014, methodology): “Inspired by the work of Tan et al. [62], Kose et al. [63] evaluated a solution based on reﬂectance to detect attacks performed with 3D masks.”
Face anti-spoofing using patch and depth-based CNNs (2017, methodology): “Common local features that have been used in prior work include LBP [30, 15, 16], HOG [25, 47], DoG [42, 36], SIFT [34] and SURF [9].”
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018, methodology): “Existing face anti-spooﬁng databases, such as NUAA [43], CASIA [50], Replay-Attack [17], and MSU-MFSD [45], were collected 3 − 5 years ago.”
Face Anti-spoofing: Visual Approach (2014, methodology): “accompanying the work of Tan and others in [13].”
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “The early works usually rely on hand-crafted features, such as LBP [14, 15, 37], SIFT [42], SURF [9], HoG [28, 52], DoG [43, 48], and traditional classifiers, such as SVM and Random Forests.”