Biometric Liveness Detection: Challenges and Research Opportunities

Akhtar, Zahid; Micheloni, Christian; Foresti, Gian Luca

doi:10.1109/msp.2015.116

Cited by 101 publications

(45 citation statements)

References 9 publications

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“…Akhtar et al [37] outlines the major challenges and open issues in biometrics concerning presentation attacks. Specifically, in case of face PAD, they discuss a wide variety of possible attacks and possible solutions.…”

Section: Multi-channel Based Approaches and Datasets For Face Padmentioning

confidence: 99%

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

George

Mostaani

Geissenbuhler

et al. 2020

IEEE Trans.Inform.Forensic Secur.

188

186

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Face recognition is a mainstream biometric authentication method. However, vulnerability to presentation attacks (a.k.a spoofing) limits its usability in unsupervised applications. Even though there are many methods available for tackling presentation attacks (PA), most of them fail to detect sophisticated attacks such as silicone masks. As the quality of presentation attack instruments improves over time, achieving reliable PA detection with visual spectra alone remains very challenging. We argue that analysis in multiple channels might help to address this issue. In this context, we propose a multi-channel Convolutional Neural Network based approach for presentation attack detection (PAD). We also introduce the new Wide Multi-Channel presentation Attack (WMCA) database for face PAD which contains a wide variety of 2D and 3D presentation attacks for both impersonation and obfuscation attacks. Data from different channels such as color, depth, near-infrared and thermal are available to advance the research in face PAD. The proposed method was compared with feature-based approaches and found to outperform the baselines achieving an ACER of 0.3% on the introduced dataset. The database and the software to reproduce the results are made available publicly.

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Section: Multi-channel Based Approaches and Datasets For Face Padmentioning

confidence: 99%

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

George

Mostaani

Geissenbuhler

et al. 2020

IEEE Trans.Inform.Forensic Secur.

188

186

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“…This approach is much cheaper and flexible [8]. While [19] divided the software-based part to 5 subcategories which are image quality, pores, skin deformation, perspiration and fusion of any ( Figure 2). …”

Section: Resultsmentioning

confidence: 99%

An investigation of fake fingerprint detection approaches

Ahmad¹,

Hassan²,

Othman³

2017

AIP Conference Proceedings

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“…Despite the great deal of progress in facial recognition systems, vulnerabilities to face spoof attacks are mainly overlooked [2]. Facial spoof attack is a process in which a fraudulent user can subvert or attack a face recognition system by masquerading as registered user and thereby gaining illegitimate access and advantages [1,[3][4][5].…”

Section: Introductionmentioning

confidence: 99%

“…Typical countermeasure to face spoof attacks is liveness detection method, which aims at disambiguating human live face samples from spoof artifacts [2,7]. There exist several face antispoofing or liveness detection techniques [7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Face Spoof Attack Recognition Using Discriminative Image Patches

Akhtar

Foresti

2016

Journal of Electrical and Computer Engineering

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Face recognition systems are now being used in many applications such as border crossings, banks, and mobile payments. The wide scale deployment of facial recognition systems has attracted intensive attention to the reliability of face biometrics against spoof attacks, where a photo, a video, or a 3D mask of a genuine user’s face can be used to gain illegitimate access to facilities or services. Though several face antispoofing or liveness detection methods (which determine at the time of capture whether a face is live or spoof) have been proposed, the issue is still unsolved due to difficulty in finding discriminative and computationally inexpensive features and methods for spoof attacks. In addition, existing techniques use whole face image or complete video for liveness detection. However, often certain face regions (video frames) are redundant or correspond to the clutter in the image (video), thus leading generally to low performances. Therefore, we propose seven novel methods to find discriminative image patches, which we define as regions that are salient, instrumental, and class-specific. Four well-known classifiers, namely, support vector machine (SVM), Naive-Bayes, Quadratic Discriminant Analysis (QDA), and Ensemble, are then used to distinguish between genuine and spoof faces using a voting based scheme. Experimental analysis on two publicly available databases (Idiap REPLAY-ATTACK and CASIA-FASD) shows promising results compared to existing works.

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Biometric Liveness Detection: Challenges and Research Opportunities

Abstract: In a spoofing attack, an impostor masquerades as a legitimate user by replicating that user's biometrics. Although methods exist to determine whether a live person or biometric artifact is in front of a biometric sensor, spoofing attacks remain a problem

Cited by 101 publications

References 9 publications

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

Biometric Face Presentation Attack Detection With Multi-Channel Convolutional Neural Network

An investigation of fake fingerprint detection approaches

Face Spoof Attack Recognition Using Discriminative Image Patches

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