Anti-spoofing in Action: Joint Operation with a Verification System

By Ivana Chingovska, André Anjos, Sébastien Marcel. 2013

74 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

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Citations in the corpus

Robust multimodal face and fingerprint fusion in the presence of spoofing attacks (2016, background): “On the mutual impact of liveness and recognition Spoofing systems are usually evaluated on their own without taking recognition into consideration [6].”
Toward an attack-sensitive tamper-resistant biometric recognition with a symmetric matcher: A fingerprint case study (2014, methodology): “Instead of using a generative approach such as BBN, in an attempt to combine face matching scores are combined with liveness measures, Chingovska et al [12] employed logistic regression as a discriminative classifier.”
Face Biometrics Under Spoofing Attacks: Vulnerabilities, Countermeasures, Open Issues, and Research Directions (2014, background): “to spoofing and does not suffer from reduced recognition accuracy [9].”
Spoofing and countermeasures for speaker verification: A survey (2015, background): “The HTER is the mean of the weighted FAR and FRR (Chingovska et al., 2013) whereas the SFAR refers to the probability of a spoofed access being falsely accepted (Johnson et al.”
Face Spoofing Detection Using Colour Texture Analysis (2016, background): “…the utilization of person-specific information either by investigating the joint operation of spoofing detection and actual face recognition stages [47] or person-specific anti-spoofing models [43], [44] that can improve both robustness and generalization capabilities of the existing…”
Biometric Antispoofing Methods: A Survey in Face Recognition (2014, background): “Although it is not yet generally deployed, an evaluation protocol which is gaining popularity for the assessment of biometric spoofing, defines two possible working scenarios [80], [107], [164], [182], [183]: • Licit Scenario, considered in classic recognition evaluations, it only takes into account”
Learn Convolutional Neural Network for Face Anti-Spoofing (2014, background): “Recently, Chingovska et al proposed to integrate face recognition module into anti-spoofing system in score-level and feature level [2].”