Biometric presentation attack detection using gaze alignment

Alsufyani, Nawal; Ali, Asad; Hoque, Sanaul; Deravi, Farzin

doi:10.1109/isba.2018.8311472

Cited by 12 publications

(10 citation statements)

References 25 publications

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“…Recently, some methods explore liveness cues to detect 3D face presentation attacks, such as thermal signatures [8], gaze information [3,5,4], and pulse or heartbeat signals [25,26,16,22]. Based on the intrinsic liveness signals, these methods achieve an outstanding performance in distinguishing real faces from masks.…”

Section: D Face Pad Methodsmentioning

confidence: 99%

A Database for Face Presentation Attack Using Wax Figure Faces

Jia

Guo

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

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Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces. Existing 3D face spoofing databases, however, mostly based on 3D masks, are restricted to small data size or poor authenticity due to the production difficulty and high cost. In this work, we introduce the first wax figure face database, WFFD, as one type of super-realistic 3D presentation attacks to spoof the FRS. This database consists of 2200 images with both real and wax figure faces (totally 4400 faces) with a high diversity from online collections. Experiments on this database first investigate the vulnerability of three popular FRS to this kind of new attack. Further, we evaluate the performance of several face presentation attack detection methods to show the attack abilities of this super-realistic face spoofing database.

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Section: D Face Pad Methodsmentioning

confidence: 99%

A Database for Face Presentation Attack Using Wax Figure Faces

Jia

Guo

et al. 2019

New Trends in Image Analysis and Processing – ICIAP 2019

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“…Ali et al [ 14 ] was the first to explore novel gaze-based approaches to detect presentation attacks. Ali et al [ 2 , 14 , 15 , 16 , 17 , 18 , 19 ] have subsequently presented a number of novel gaze-based approaches to presentation attack detection. A visual target with randomly assigned trajectories were shown on the display screen for the user to follow with eye (and head) movements.…”

Section: State Of the Artmentioning

confidence: 99%

“…Three types of attack artefacts were used here in order to evaluate the proposed techniques. The attack scenarios assume an impostor attempting to subvert the biometric system by displaying a high-resolution image of a genuine user on a tablet screen (photo attack), or a high-quality printed colour photo with holes in place of the pupils held in front of the impostor’s face as a mask (2D mask attack) or presenting a three-dimensional mask constructed using the genuine user’s data (3D mask attack) [ 17 ].…”

Section: Experimental Evaluationmentioning

confidence: 99%

Directed Gaze Trajectories for Biometric Presentation Attack Detection

Ali

Hoque

Deravi

2021

Sensors

Self Cite

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Presentation attack artefacts can be used to subvert the operation of biometric systems by being presented to the sensors of such systems. In this work, we propose the use of visual stimuli with randomised trajectories to stimulate eye movements for the detection of such spoofing attacks. The presentation of a moving visual challenge is used to ensure that some pupillary motion is stimulated and then captured with a camera. Various types of challenge trajectories are explored on different planar geometries representing prospective devices where the challenge could be presented to users. To evaluate the system, photo, 2D mask and 3D mask attack artefacts were used and pupillary movement data were captured from 80 volunteers performing genuine and spoofing attempts. The results support the potential of the proposed features for the detection of biometric presentation attacks.

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“…There are relatively many methods based on human-computer interaction to prevent spoofing attacks. For example, Alsufyani et al [2] used the random movement of infrared light to track the relative movement trajectory of the human eye. Singh et al [3] and Pan et al [4] proposed to detect the user's blinking and lip movements to resist people face spoofing attack; Tirunagari et al [5] used dynamic correlation models to preprocess the video to extract texture features.…”

Section: Introductionmentioning

confidence: 99%

Research on Face Anti-Spoofing Algorithm Based on Image Fusion

Yu¹,

Wang²,

Cao³

et al. 2021

Computers, Materials &Amp; Continua

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Along with the rapid development of biometric authentication technology, face recognition has been commercially used in many industries in recent years. However, it cannot be ignored that face recognition-based authentication techniques can be easily spoofed using various types of attacks such photographs, videos or forged 3D masks. In order to solve this problem, this work proposed a face anti-fraud algorithm based on the fusion of thermal infrared images and visible light images. The normal temperature distribution of the human face is stable and characteristic, and the important physiological information of the human body can be observed by the infrared thermal images. Therefore, based on the thermal infrared image, the pixel value of the pulse sensitive area of the human face is collected, and the human heart rate signal is detected to distinguish between real faces and spoofing faces. In order to better obtain the texture features of the face, an image fusion algorithm based on DTCWT and the improved Roberts algorithm is proposed. Firstly, DTCWT is used to decompose the thermal infrared image and visible light image of the face to obtain high-and low-frequency subbands. Then, the method based on region energy and the improved Roberts algorithm are then used to fuse the coefficients of the high-and low-frequency subbands. Finally, the DTCWT inverse transform is used to obtain the fused image containing the facial texture features. Face recognition is carried out on the fused image to realize identity authentication. Experimental results show that this algorithm can effectively resist attacks from photos, videos or masks. Compared with the use of visible light images alone for face recognition, this algorithm has higher recognition accuracy and better robustness.

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Biometric presentation attack detection using gaze alignment

Cited by 12 publications

References 25 publications

A Database for Face Presentation Attack Using Wax Figure Faces

A Database for Face Presentation Attack Using Wax Figure Faces

Directed Gaze Trajectories for Biometric Presentation Attack Detection

Research on Face Anti-Spoofing Algorithm Based on Image Fusion

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