Non-intrusive liveness detection by face images

Kollreider, K.; Fronthaler, H.; Bigün, Josef

doi:10.1016/j.imavis.2007.05.004

Cited by 159 publications

(109 citation statements)

References 25 publications

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“…In [17], Kollreider and others present a technique to evaluate liveness based on a short sequence of images, also leveraging from OF analysis. The work describes a binary detector that evaluates the trajectories of select parts of the face presented to the input sensor using a simplified OF estimator followed by an heuristic classifier, with excellent results reported on a private dataset derived from the XM2VTS dataset (∼1.5% equal error rate with a threshold chosen a posteriori).…”

Section: Literature Surveymentioning

confidence: 99%

“…The only parameter that should be tunned according to [17] is the value of the threshold (henceforth denominated α). Furthermore, the authors indicate that such a value should be searched in the range Table II.…”

Section: ) Feature Extractionmentioning

confidence: 99%

“…There, one can verify this algorithm only works as advertised if attacks are performed with one's own hands once more. Indeed, the working hypothesis for both RS1 [17] and RS2 [16] is that real clients trying to authenticate display motion differently than photo attacks. In a scenario where clients are still or have unperceivable motion and attacks are drawn from fixed stands, that assumption can no longer be true.…”

Section: )mentioning

confidence: 99%

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Motion‐based counter‐measures to photo attacks in face recognition

2014

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Identity spoofing is a contender for high-security face recognition applications. With the advent of social media and globalized search, our face images and videos are wide-spread on the internet and can be potentially used to attack biometric systems without previous user consent. Yet, research to counter these threats is just on its infancy -we lack public standard databases, protocols to measure spoofing vulnerability and baseline methods to detect these attacks. The contributions of this work to the area are three-fold: firstly we introduce a publicly available PHOTO-ATTACK database with associated protocols to measure the effectiveness of counter-measures. Based on the data available, we conduct a study on current state-of-the-art spoofing detection algorithms based on motion analysis, showing they fail under the light of these new dataset. By last, we propose a new technique of countermeasure solely based on foreground/background motion correlation using Optical Flow that outperforms all other algorithms achieving nearly perfect scoring with an equal-error rate of 1.52% on the available test data. The source code leading to the reported results is made available for the replicability of findings in this article.

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Section: Literature Surveymentioning

confidence: 99%

Section: ) Feature Extractionmentioning

confidence: 99%

Section: )mentioning

confidence: 99%

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Motion‐based counter‐measures to photo attacks in face recognition

2014

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“…Pan et al [13] use eye-blinking detection for a liveness test based on a probabilistic model of eye-blinking behaviour. In [14] optical flow lines are used to model and distinguish between live face movement and still image movement. Lai and Tai [15] recently proposed a liveness test against attacks by fake images or videos displayed on HD screens by analyzing the chrominance characteristics and the saturation of the face recognition system's input images.…”

Section: Related Workmentioning

confidence: 99%

Designing a Facial Spoofing Database for Processed Image Attacks

Omar¹,

Ivrissimtzis²

2016

7th International Conference on Imaging for Crime Detection and Prevention (ICDP 2016)

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractFace recognition systems are used for user authentication in everyday applications such as logging into a laptop or smartphone without need to memorize a password. However, they are still vulnerable to spoofing attacks, as for example when an imposter gains access to a system by holding a printed photo of the rightful user in front of the camera. In this paper we are concerned with the design of face image databases for evaluating the performance of anti-spoofing algorithms against such attacks. We present a new database, supporting testing against an enhancement of the attack, where the imposter processes the stolen image before printing it. By testing a standard antispoofing algorithm on the new database we show a significant decrease in its performance and, as a simple remedy to this problem, we propose the inclusion of processed imposter images into the training set.

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“…Kollreider et al [13] present a motion based countermeasure that estimates the correlation between different regions of the face using optical flow. In that countermeasure, the input is considered a spoof if the optical flow field on the center of the face and on the center of the ears present the same direction.…”

Section: Prior Workmentioning

confidence: 99%

LBP − TOP Based Countermeasure against Face Spoofing Attacks

Pereira

Anjos

Martino

et al. 2013

Computer Vision - ACCV 2012 Workshops

179

114

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Abstract. User authentication is an important step to protect information and in this field face biometrics is advantageous. Face biometrics is natural, easy to use and less human-invasive. Unfortunately, recent work has revealed that face biometrics is vulnerable to spoofing attacks using low-tech cheap equipments. This article presents a countermeasure against such attacks based on the LBP − T OP operator combining both space and time information into a single multiresolution texture descriptor. Experiments carried out with the REPLAY ATTACK database show a Half Total Error Rate (HT ER) improvement from 15.16% to 7.60%.

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Non-intrusive liveness detection by face images

Cited by 159 publications

References 25 publications

Motion‐based counter‐measures to photo attacks in face recognition

Motion‐based counter‐measures to photo attacks in face recognition

Designing a Facial Spoofing Database for Processed Image Attacks

LBP − TOP Based Countermeasure against Face Spoofing Attacks

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