Face anti-spoofing using patch and depth-based CNNs

By Yousef Atoum, Yaojie Liu, Amin Jourabloo, Xiaoming Liu. 2017

426 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

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Citations in the corpus

Detection and Continual Learning of Novel Face Presentation Attacks (2021, background): “Similar to most sub-fields in computer vision, advances in deep learning, which is inspired by the nervous system [17], have led to significant face PAD improvements on benchmark datasets using convolutional neural network-based (CNN) end-to-end representation learning and classification [1, 7, 12, ”
Deep Learning in the Wild (2018, methodology): “In more recent work, deep learning methods have been introduced [3,64,63,31].”
Searching Central Difference Convolutional Networks for Face Anti-Spoofing (2020, methodology): “In order to solve this issue, several depth supervised FAS methods [4, 36], which utilize pseudo depth map label as auxiliary supervised signal, have been developed.”
Deep Models and Shortwave Infrared Information to Detect Face Presentation Attacks (2020, methodology): “For instance, depth information has been used in conjunction with color images in [15].”
Face Anti-Spoofing: Model Matters, so Does Data (2019, methodology): “Different from previous random method [2] or fixed setting [25], we learn the discriminative region attention via a deep module RAM.”
Face Anti-Spoofing with Human Material Perception (2020, background): “In contrast, auxiliary depth [10,16,14] and re-ﬂection [28] supervisions are introduced to learn detailed cues eﬀectively.”
Density-Aware Single Image De-raining Using a Multi-stream Dense Network (2018, background): “leveraged in various applications such as semantic segmentation [45], face-alignment [22], visual tracking [18] crowdcounting [30], single image super-resolution[43], face antiSpoofing [1], action recognition [48], depth estimation [5], single image dehazing [24, 42, 40] and also in single image de-”
Deep Spatial Gradient and Temporal Depth Learning for Face Anti-Spoofing (2020, methodology): “In [2], the depth map of a face is utilized as a supervisory signal for the first time.”
Face De-Spoofing: Anti-Spoofing via Noise Modeling (2018, background): “Here the 6 channels are RGB + HSV color space, following the suggestion in [31].”
Cross-Domain Face Presentation Attack Detection via Multi-Domain Disentangled Representation Learning (2020, background): “To improve the robustness of PAD under unseen scenarios, some scenario-invariant auxiliary information (such as face depth and heart rhythm) has been exploited for distinguishing be-tween live and spoof faces [2, 30, 29].”
Learning Deep Models for Face Anti-Spoofing: Binary or Auxiliary Supervision (2018, background): “We advance [5] in a number of aspects, including fusion with temporal supervision (i.”
Single-Side Domain Generalization for Face Anti-Spoofing (2020, background): “[3] propose a two-stream CNN architecture to extract depth features combining with the texture features to detect attacks.”
Deep Tree Learning for Zero-Shot Face Anti-Spoofing (2019, methodology): “Recent works [2, 4] augment the data by using image patches, and fuse the scores from patches to a single decision.”
On the Detection of Digital Face Manipulation (2019, background): “i) Physical spoofing attacks can be as simple as face printed on a paper, replaying image/video on a phone, or as complicated as a 3D mask [8, 24, 34, 35].”