Face liveness detection based on texture and frequency analyses

Kim, Gahyun; Eum, Sungmin; Suhr, Jae Kyu; Kim, Dong Ik; Park, Kang Ryoung; Kim, Jaihie

doi:10.1109/icb.2012.6199760

Cited by 93 publications

(58 citation statements)

References 13 publications

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“…The authors reported 0% HTER on the Print Attack dataset. In other research [7], power spectrum and LBP features were used in a fusion approach on a print attack database collected using a camera of an automated teller machine.…”

Section: Introductionmentioning

confidence: 99%

Computationally Efficient Face Spoofing Detection with Motion Magnification

Bharadwaj

Dhamecha

Vatsa

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

202

127

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For a robust face biometric system, a reliable antispoofing approach must be deployed to circumvent the print and replay attacks. Several techniques have been proposed to counter face spoofing, however a robust solution that is computationally efficient is still unavailable. This paper presents a new approach for spoofing detection in face videos using motion magnification. Eulerian motion magnification approach is used to enhance the facial expressions commonly exhibited by subjects in a captured video. Next, two types of feature extraction algorithms are proposed: (i) a configuration of LBP that provides improved performance compared to other computationally expensive texture based approaches and (ii) motion estimation approach using HOOF descriptor. On the Print Attack and Replay Attack spoofing datasets, the proposed framework improves the state-of-art performance; especially HOOF descriptor yielding a near perfect half total error rate of 0% and 1.25% respectively.

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Section: Introductionmentioning

confidence: 99%

Computationally Efficient Face Spoofing Detection with Motion Magnification

Bharadwaj

Dhamecha

Vatsa

et al. 2013

2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops

202

127

View full text Add to dashboard Cite

show abstract

“…The approach analyses the texture of facial images using multiscale local binary patterns operators and is evaluated on the NUAA publicly available data set. The work in [33] proposes a single image-based face liveness detection method for discriminating 2D paper masks from the live faces. The method exploits frequency and texture information using power spectrum and LBPs.…”

Section: Related Workmentioning

confidence: 99%

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Arashloo

Kittler

Christmas

2015

IEEE Trans.Inform.Forensic Secur.

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Abstract-Face recognition has been the focus of attention for the past couple of decades and, as a result, a significant progress has been made in this area. However, the problem of spoofing attacks can challenge face biometric systems in practical applications. In this work, an effective countermeasure against face spoofing attacks based on a kernel discriminant analysis approach is presented. Its success derives from three innovations. First it is shown that the recently proposed multiscale dynamic texture descriptor based on binarized statistical image features on three orthogonal planes (MBSIF-TOP) is effective in detecting spoofing attacks, showing promising performance compared to existing alternatives.Next, by combining MBSIF-TOP with a blur-tolerant descriptor, namely the dynamic multiscale local phase quantization representation (MLPQ-TOP), the robustness of the spoofing attack detector can be further improved. The fusion of the information provided by MSIF-TOP and MLPQ-TOP is realized via a kernel fusion approach based on a fast kernel discriminant analysis (KDA) technique. It avoids the costly eigenanalysis computations by solving the KDA problem via spectral regression. The experimental evaluation of the proposed system on different databases demonstrates its advantages in detecting spoofing attacks in various imaging conditions, compared to existing methods.

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“…In [20], Kim et al explored two key observations: (1) the difference in the existence of 3D shapes leads to the difference in low frequency regions which is closely related to the luminance component; and (2) the difference between real and fake faces generates a disparity in the high frequency information. The motivation for using texture information lies in the fact that printed faces tend to loose the richness of texture details.…”

Section: Texture and Frequency Analysismentioning

confidence: 99%

Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks

Pinto

Schwartz

Pedrini

et al. 2015

IEEE Trans.Inform.Forensic Secur.

115

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Spoofing attacks or impersonation can be easily accomplished in a facial biometric system wherein users without access privileges attempt to authenticate themselves as valid users, in which an impostor needs only a photograph or a video with facial information of a legitimate user. Even with recent advances in biometrics, information forensics and security, vulnerability of facial biometric systems against spoofing attacks is still an open problem. Even though several methods have been proposed for photo-based spoofing attack detection, attacks performed with videos have been vastly overlooked, which hinders the use of the facial biometric systems in modern applications. In this paper, we present an algorithm for video-based spoofing attack detection through the analysis of global information which is invariant to content, since we discard video contents and analyze contentindependent noise signatures present in the video related to the unique acquisition processes. Our approach takes advantage of noise signatures generated by the recaptured video to distinguish between fake and valid access videos. For that, we use the Fourier spectrum followed by the computation of video visual rhythms and the extraction of different characterization methods. For evaluation, we consider the novel Unicamp Video-Attack Database (UVAD) which comprises 17,076 videos composed of real access and spoofing attack videos. In addition, we evaluate the proposed method using the Replay-Attack Database, which contains photo-based and video-based face spoofing attacks.

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Face liveness detection based on texture and frequency analyses

Cited by 93 publications

References 13 publications

Computationally Efficient Face Spoofing Detection with Motion Magnification

Computationally Efficient Face Spoofing Detection with Motion Magnification

Face Spoofing Detection Based on Multiple Descriptor Fusion Using Multiscale Dynamic Binarized Statistical Image Features

Using Visual Rhythms for Detecting Video-Based Facial Spoof Attacks

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