Few-Shot Face Spoofing Detection Using Feedforward Learning Network

doi:10.13190/j.jbupt.2020-068

Journal of Beijing University of Posts and Telecommunications ›› 2020, Vol. 43 ›› Issue (5): 48-56.doi: 10.13190/j.jbupt.2020-068

• PAPERS • Previous Articles Next Articles

Few-Shot Face Spoofing Detection Using Feedforward Learning Network

SONG Yu, SUN Wen-yun, CHEN Chang-sheng

1. College of Electronics and Information Engineering, Shenzhen University, Shenzhen 518060, China;
2. Shenzhen Key Laboratory of Media Security, Shenzhen University, Shenzhen 518060, China;
3. Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China;
4. Guangdong Laboratory of Artificial Intelligence and Digital Economy, Shenzhen University, Shenzhen 518060, China;
5. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen 518060, China

Received:2020-06-15 Published:2021-03-11

Abstract

Abstract: In order to overcome the limitations of the existing face spoofing detection methods under few-shot face anti-spoofing applications, this paper proposes to use feedforward learning network for face anti-spoofing. The convolutional filters are learned unsupervisedly from the images in a feedforward manner. The feedforward learning network is adapted in the spoof face detection applications by using face anti-spoofing task-oriented convolutional filters learned from the training images. The eigenvectors that correspond to the smallest eigenvalues obtained from the principle component analysis transform are used as convolution filters for extracting features from images. The method is evaluated on some benchmark datasets including CASIA-FASD dataset, Idiap Replay-Attack dataset and OULU-NPU dataset. Experiments show that under the cross presentation attack detection experiments, the proposed method significantly improves the classification accuracy of existing methods.

Key words: face spoofing detection, feedforward learning network, representation learning

CLC Number:

SONG Yu, SUN Wen-yun, CHEN Chang-sheng. Few-Shot Face Spoofing Detection Using Feedforward Learning Network[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(5): 48-56.

References

[1] Chan T H, Jia K, Cao S, et al. PCANet:a simple deep learning baseline for image classification?[J]. IEEE Transactions on Image Processing, 2015, 24(12):5017-5032.
[2] Boulkenafet Z, Komulainen J, Hadid A. Face anti-spoofing based on color texture analysis[C]//International Conference on Biometrics. Phuket:IEEE, 2015:2636-2640.
[3] Boulkenafet Z, Komulainen J, Hadid A. Face spoofing detection using color texture analysis[J]. IEEE Transactions on Information Forensics and Security, 2016, 11(8):1818-1830.
[4] Boulkenafet Z, Komulainen J, Hadid A. Face antispoofing using speeded-up robust features and fish vector encoding[J]. IEEE Signal Processing Letters, 2017, 24(2):141-145.
[5] Garcia D C, Queiroz R L. Face-spoofing 2D-detection based on moire-pattern analysis[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(4):778-786.
[6] Galbally J, Marcel S, Fierrez J. Image quality assessment for fake biometric detection:application to iris, fingerprint, and face recognition[J]. IEEE Transactions on Image Processing, 2014, 23(2):710-724.
[7] Wen D, Han H, Jain A K. Face spoof detection with image distortion analysis[J]. IEEE Transactions on Information Forensics and Security, 2015, 10(4):746-761.
[8] Rehman Y A U, Po L M, Liu M. Deep learning for face anti-spoofing:an end-to-end approach[C]//IEEE Conference on Signal Processing. Poznan:IEEE, 2017:195-200.
[9] Nagpal C, Dubey S R. A performance evaluation of convolutional neural networks for face anti spoofing[J/OL].[2019-03-27]. https://arxiv.org/abs/1805.04176.
[10] Atoum Y, Liu Y, Jourabloo A, et al. Face anti-spoofing using patch and depth-based CNNs[C]//IEEE International Joint Conference on Biometrics. Denver:IEEE, 2017:319-328.
[11] Liu Y, Jourabloo A, Liu X. Learning deep models for face anti-spoofing:binary or auxiliary supervision[C]//IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City:IEEE, 2018:389-398.
[12] George A, Marcel S. Deep pixel-wise binary supervision for face presentation attack detection[J/OL].[2019-07-09]. https://arxiv.org/abs/1907.04047.
[13] Li L, Feng X, Jiang X, et al. Face anti-spoofing via deep local binary patterns[C]//International Conference on Image Processing. Beijing:IEEE, 2017:101-105.
[14] Li H, Li W, Cao H, et al. Unsupervised domain adaptation for face anti-spoofing[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(7):1794-1809.
[15] Zhang Z, Yan J, Liu S, et al. A face antispoofing database with diverse attacks[C]//International Conference on Biometrics. New Delhi:IEEE, 2012:26-31.
[16] Chingovska I, Anjos A, Marcel S. On the effectiveness of local binary patterns in face anti-spoofing[C]//IEEE International Conference of Biometrics Special Interest Group. Darmstadt:IEEE, 2012:183-194.
[17] Boulkenafet Z, Komulainen J, Li L, et al. OULU-NPU:a mobile face presentation attack database with real-world variations[C]//IEEE International Conference on Automatic Face and Gesture Recognition. Washington, DC:IEEE, 2017:612-618.
[18] Viola P, Jones M. Rapid object detection using a boosted cascade of simple features[C]//IEEE Conference on Computer Vision and Pattern Recognition. Kauai:IEEE, 2001:511-518.

Few-Shot Face Spoofing Detection Using Feedforward Learning Network

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 2

Recommended Articles

Metrics

Comments

[1]	HUO Weigang, MENG Lu, GUO Runxia. The Method of Multivariable Time Series Representation Learning Combining Reconstruction with Contrasting [J]. Journal of Beijing University of Posts and Telecommunications, 2023, 46(4): 83-90.
[2]	LIU Linlan, SONG Xiuyang, CHEN Yubin. Link Prediction in Opportunistic Networks Based on Network Representation Learning [J]. Journal of Beijing University of Posts and Telecommunications, 2022, 45(4): 64-69,103.