The imaging issue in an automatic face/disguise detection system

By Ioannis T. Pavlidis, Peter F. Symosek. 2000

161 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, neutral): “Pavlidis Symosek [7] 2000 − Facial disguises 3DMAD [5] 2013 17 3D mask attacks I(2)BVSD [4] 2013 75 Facial disguises GUC-LiFFAD [8] 2015 80 2D print and replay MS-Spoof [3] 2015 21 2D print BRSU [11] 2016 50 3D masks EMSPAD [9] 2017 50 2D print MLFP [1] 2017 10 2D & 3D masks CASIA-SURF [12] 2020 100”
Face Presentation Attack with Latex Masks in Multispectral Videos (2017, background): “In the literature, thermal imagery has shown promising results for detecting disguise variations [7], [18].”
A face antispoofing database with diverse attacks (2012, background): “It is important to mention that multi-spectral [7, 8, 9] and multi-modality [10, 11] based anti-spoofing methods also perform quite well by exploring other liveness clues such as face images under nonvisible illuminations or voice information.”
Recognizing Disguised Faces: Human and Machine Evaluation (2014, background): “From automated face recognition point of view, Pavlidis and Symosek [44] have suggested that detecting disguise is necessary to efficiently recognize disguised faces.”
Face Anti-spoofing: Visual Approach (2014, background): “While multimodal analysis [23–25] and multispectral imaging [26–28] provide efficient means for discriminating real faces from fake ones, spoofing detection can be performed also based on the same data that is used for the actual face recognition process.”
Face liveness detection with component dependent descriptor (2013, background): “In [20, 26], it is proved that indistinguishable faces exhibit much different properties from the genuine ones under invisible light.”
Counter-measures to photo attacks in face recognition: A public database and a baseline (2011, background): “Despite the fact that solutions exist for spoof prevention using multi-modal techniques [5, 6, 7, 8], it is our belief that research for counter-measures solely based on unimodal 2-D imagery has not yet reached a matured state.”
Recent advances in visual and infrared face recognition - a review (2005, background): “4 lm may provide useful information for detecting disguised faces [15] due to unique and universal properties of the human skin in this subband.”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, background): “However, some interesting methods have been proposed base on imaging technology outside the visual spectrum, such as: complementary infrared (IR) or near infrared (NIR) images, which are even claimed to provide sufficient information to distinguish between identical twins [129], [152]; comparing the”
Design of an Active Multispectral SWIR Camera System for Skin Detection and Face Verification (2016, background): “…use of the SWIR spectral range can be classified into four groups: multispectral image acquisition using multiple cameras with band pass filters [9, 12], hyperspectral imagers [13], single cameras using filter-wheels with band pass filters for sequential multispectral image acquisition [14],…”
Learn Convolutional Neural Network for Face Anti-Spoofing (2014, background): “In [26] and [37], the authors selected proper working spectrums so that the reﬂectance differences between genuine and fake faces increased.”
Face liveness detection by learning multispectral reflectance distributions (2011, methodology): “In [3] Pavlidis and Symosekuses uses light at two wavelengths, and a simple threshold method to detect the genuine and fake faces.”
Spoofing Face Recognition With 3D Masks (2014, background): “Later, stating that disguises can be detected even better in near-infrared, Pavlidis and Symosek propose to utilize the 1.3-1.7 µm sub-band of the upper band [18].”
Context based face anti-spoofing (2013, background): “While challenge-response approach [7, 13, 16], multi-modal analysis [5, 12, 16] and multispectral imaging [22, 24, 29] provide efficient means for discriminating real faces from fake ones, they are also rather impractical due to interaction or unconventional imaging requirements.”
Face Spoofing Detection Using Colour Texture Analysis (2016, background): “Hardware-based solutions using 3D [18] or multi-spectral [19], [20] imaging provide efficient means for detecting face spoofs because they offer additional useful information on the surface reflectance properties or depth of the observed face.”