Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera

By Gang Pan, Lin Sun, Zhaohui Wu, Shihong Lao. IEEE International Conference on Computer Vision, 2007

689 citations recorded by the source corpus.

Subfields: Neurology, Human-Computer Interaction, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b9bffe19500a99c8d41a1423a3a87a9586eafcc6

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Citations in the corpus

Real-Time Eye Blink Detection using Facial Landmarks (2016, background): “We consider half blinks, which do not achieve the close state, as full blinks to be consistent with the ZJU.”
Counter-measures to photo attacks in face recognition: A public database and a baseline (2011, background): “[18] and [19] bring a real-time liveness detection specifically against photo-spoofing using (spontaneous) eye-blinks which are supposed to occur once every 2-4 seconds in humans.”
In Ictu Oculi: Exposing AI Created Fake Videos by Detecting Eye Blinking (2018, background): “Similarly, the ZJU Eyeblink Video Database [23] is not accessible.”
CASIA-SURF: A Large-Scale Multi-Modal Benchmark for Face Anti-Spoofing (2019, background): “Some previous works [24]–[27] attempt to detect the evidence of liveness (e.”
Secure Face Unlock: Spoof Detection on Smartphones (2016, methodology): “Additionally, they are based on databases in which the spoof videos were captured using either low resolution (e.g., webcam) or very high-resolution (e.g., DSLR) cameras [4], [7] (e.g., the CASIA FASD and Idiap databases released in 2012).”
Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018, background): “The cue-based methods aim to detect liveness cues [1,2] (e.”
A face antispoofing database with diverse attacks (2012, background): “Among the above works, [12] used warped photos as attack; [4] designed several fake faces, which include warped photo attack and cut photo attack.”
Single-Side Domain Generalization for Face Anti-Spoofing (2020, background): “Mouthmotion detection [17] and eye-blinking detection [28, 34] are among the earliest solutions for face anti-spoofing based on the temporal cues.”
Face liveness detection with component dependent descriptor (2013, background): “This response can be represented by biometric motions, such as eye blinking [23, 18], head rotation [2], mouth movement [13], and the holistic motion [12].”
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “For example in [40, 41], the eye-blinking fact is used to predict spoofing.”
Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, methodology): “Several classical local 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].”
Competition on counter measures to 2-D facial spoofing attacks (2011, background): “[18, 19] bring a real-time liveness detection specifically against photo-spoofing using eye-blinks.”
Spoofing in 2D face recognition with 3D masks and anti-spoofing with Kinect (2013, background): “The first group aims to detect liveness of face, based on live-face specific movements such as eye blinking [17] or lip movements [5].”
Human Identification Using Palm-Vein Images (2011, background): “Therefore, palmprint research has invited a lot of attention for civilian and forensic usage [1].”