Face anti-spoofing via deep local binary patterns

Li, Lei; Feng, Xiaoyi; Jiang, Xiaoyue; Xia, Zhaoqiang; Hadid, Abdenour

doi:10.1109/icip.2017.8296251

Cited by 33 publications

(14 citation statements)

References 10 publications

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“…Different color spaces might lead to different performance of anti-spoofing [48], though RGB color is the most widely used. To explore the effect of color space, we conduct experiments and compare the performance of three color spaces: RGB, HSV and YCbCr.…”

Section: Results Of Casia-fasdmentioning

confidence: 99%

Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

Chen

Lei

et al. 2020

IEEE Trans.Inform.Forensic Secur.

166

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Section: Results Of Casia-fasdmentioning

confidence: 99%

Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

Chen

Lei

et al. 2020

IEEE Trans.Inform.Forensic Secur.

166

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“…Apart from that, some feature descriptors or indexes are computed to describe the clarity of facial texture [9,8,21]. More recently, many attempts of using CNN-based features in face PAD [22,23,24]. While these methods are effective to typical 2D paper or replayed attacks, they become vulnerable when attackers wear a lifelike face mask.…”

Section: Methods For 2d Face Padmentioning

confidence: 99%

3D face mask presentation attack detection based on intrinsic image analysis

Xia

Jiang

et al. 2020

IET biom.

Self Cite

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Face presentation attacks have become a major threat to face recognition systems and many countermeasures have been proposed in the past decade. However, most of them are devoted to 2D face presentation attacks, rather than 3D face masks. Unlike the real face, the 3D face mask is usually made of resin materials and has a smooth surface, resulting in reflectance differences. So, we propose a novel detection method for 3D face mask presentation attack by modeling reflectance differences based on intrinsic image analysis. In the proposed method, the face image is first processed with intrinsic image decomposition to compute its reflectance image. Then, the intensity distribution histograms are extracted from three orthogonal planes to represent the intensity differences of reflectance images between the real face and 3D face mask. After that, the 1D convolutional network is further used to capture the information for describing different materials or surfaces react differently to changes in illumination. Extensive experiments on the 3DMAD database demonstrate the effectiveness of our proposed method in distinguishing a face mask from the real one and show that the detection performance outperforms other state-of-the-art methods.

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“…With the application of deep learning in computer vision, some researchers apply it to face presentation attack detection and achieve better results, such as deep local binary patterns [27], partial convolutional neural network [28], hybrid convolutional neural network [29].…”

Section: Related Workmentioning

confidence: 99%

Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection

Liu

2020

IEEE Access

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With their growing popularity and widespread applications, face recognition systems are attracting more attention from attackers. Thus, face presentation attack detection has emerged as an important research topic in recent years. Existing methods for face presentation attack detection are affected by different cameras and display devices, and their performance is degraded in cross-database testing. In this paper, we propose a face presentation attack detection scheme that fuses multi-perspective dynamic features. One feature is the globally extracted temporal motion pattern of a face in a video. This involves mapping the local and global motion information of the face in the video into a single image. The motion patterns of genuine and fake faces are different, and these patterns are independent of cameras and display devices. Another feature is the visual rhythm of noise patterns, which differs significantly between single and secondary imaging. The proposed scheme fuses these two features at the decision level. Cross-database tests were conducted among the CASIA-FASD, MSU-MFSD and Replay-Attack databases. The experimental results show that the proposed scheme outperforms state-of-the-art algorithms. INDEX TERMS Face presentation attack detection, multi-perspective features, visual rhythm, noise pattern, motion pattern.

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Face anti-spoofing via deep local binary patterns

Cited by 33 publications

References 10 publications

Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

Attention-Based Two-Stream Convolutional Networks for Face Spoofing Detection

3D face mask presentation attack detection based on intrinsic image analysis

Multi-Perspective Dynamic Features for Cross-Database Face Presentation Attack Detection

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