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http://dx.doi.org/10.5909/JBE.2019.24.6.1113

Face Anti-Spoofing Based on Combination of Luminance and Chrominance with Convolutional Neural Networks  

Kim, Eunseok (Department of Electrical and Electronics Engineering, Konkuk University)
Kim, Wonjun (Department of Electrical and Electronics Engineering, Konkuk University)
Publication Information
Journal of Broadcast Engineering / v.24, no.6, 2019 , pp. 1113-1121 More about this Journal
Abstract
In this paper, we propose the face anti-spoofing method based on combination of luminance and chrominance with convolutional neural networks. The proposed method extracts luminance and chrominance features independently from live and fake faces by using stacked convolutional neural networks and auxiliary networks. Unlike previous methods, an attention module has been adopted to adaptively combine extracted features instead of simply concatenating them. In addition, we propose a new loss function, called the contrast loss, to learn the classifier more efficiently. Specifically, the contrast loss improves the discriminative power of the features by maximizing the distance of the inter-class features while minimizing that of the intra-class features. Experimental results demonstrate that our method achieves the significant improvement for face anti-spoofing compared to existing methods.
Keywords
face anti-spoofing; luminance and chrominance; convolutional neural networks; attention module; contrast loss;
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1 A. K. Jain, K. Nandakumar, and A. Ross, "50 years of biometric research: Accomplishments, challenges, and opportunities," Pattern Recognit. Lett., vol. 79, pp. 80-105, Aug. 2015.   DOI
2 J.-W. Li, "Eye blink detection based on multiple Gabor response waves," in Proc. Int. Conf. Mach. Learn. Cybern. (ICMLC), Jul. 2008, pp. 2852-2856.
3 G. Chetty and M. Wagner, "Multi-level liveness verification for face-voice biometric authentication," in Proc. Biometrics Symp., Special Session Res. Biometric Consortium Conf., Sep. 2006, pp. 1-6.
4 K. Kollreider, H. Fronthaler, and J. Bigun, "Evaluating liveness by face images and the structure tensor," in Proc. 4th IEEE Workshop Automat. Identificat. Adv. Technol., Oct. 2005, pp. 75-80.
5 J. Galbally, S. Marcel, and J. Fierrez, "Image quality assessment for fake biometric detection: application to iris, fingerprint, and face recognition," IEEE Trans. Image Process. vol. 23, no. 2, pp. 710-724, Feb. 2014.   DOI
6 T. Ojala, M. Pietikainen, and T. Maenpaa, "Multiresolution gray-scale and rotation invariant texture classification with local binary patterns," IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, no. 7, pp. 971-987, Jul. 2002.   DOI
7 J. Maatta, A. Hadid, and M. Pietikainen, "Face spoofing detection from single images using micro-texture analysis," in Proc. Int. Joint Conf. Biometrics (IJCB), Oct. 2011, pp. 1-7.
8 D. Gragnaniello, G. Poggi, C. Sansone, and L. Verdoliva, "An investigation of local descriptors for biometric spoofing detection," IEEE Trans. Inf. Forensics Security, vol. 10, no. 4, pp. 849-863, Apr. 2015.   DOI
9 O. Lucena, A. Junior, V. Moia, R. Souza, E. Valle, and R. Lotufo, "Transfer learning using convolutional neural networks for face antispoofing," in Proc. Int. Conf. Image Anal. Recognit., Jun. 2017, pp. 27-34.
10 J. Yang, Z. Lei, and S. Z. Li, "Learn convolutional neural network for face anti-spoofing," arXiv preprint arXiv: 1408.5601, Aug. 2014.
11 J. Hu, L. Shen, and G. Sun, "Squeeze-and-Excitation Networks," in Proc. IEEE Conf. Comput. Vis. Pattern Recog. (CVPR), June. 2018, pp. 7132-7141.
12 P. Viola and M. Jones, "Rapid object detection using a boosted cascade of simple features," in Proc. IEEE Int. Conf. Comput. Vis. Pattern Recognit. (CVPR), Dec. 2001, pp. I-511-I-518.
13 Z. Boulkenafet, J. Komulainen, and A. Hadid, "Face anti-spoofing using color texture analysis," IEEE Trans. Inf. Forensics Security, vol. 11 no. 8, pp. 1818-1830, Aug. 2016.   DOI
14 Z. Xu, S. Li, and W. Deng, "Learning temporal features using lstm-cnn architecture for face anti-spoofing," in Proc. IAPR Asian Conf. Pattern Recognit. (ACPR), Nov. 2015, pp. 141-145.
15 X. Zhao, Y. Lin, and J. Heikkila, "Dynamic texture recognition using volume local binary count patterns with an application to 2D face spoofing detection," IEEE Trans. Multimedia, vol. 20, no. 3, pp. 552-566, Mar. 2017   DOI
16 J. Gan, S. Li, Y. Zhai, and C. Liu, "3D convolutional neural network based on face anti-spoofing," in Proc. Int. Conf. Multimedia Image Process. (ICMIP), Mar. 2017, pp. 1-5.
17 Z. Zhang, J. Yan, S. Liu, Z. Lei, D Yi, S. Z. Li. "A Face Antispoofing Database with Diverse Attacks." in Proc. Int. Conf. Biometrics (ICB), Mar. 2012, pp. 26-31