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Galbally, S. Marcel, and J. Fierrez, \u201cBiometric Antispoofing Methods: A Survey in Face Recognition,\u201d IEEE Access, vol.2, pp.1530-1552, 2014. 10.1109\/access.2014.2381273","DOI":"10.1109\/ACCESS.2014.2381273"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] Z. Wu, N. Evans, T. Kinnunen, J. Yamagishi, F. Alegrem, and H. Li, \u201cSpoofing and Countermeasures for Speaker Verification: A Survey,\u201d Speech Communication, vol.66, pp.130-153, 2015. 10.1016\/j.specom.2014.10.005","DOI":"10.1016\/j.specom.2014.10.005"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] E. Marasco and A. Ross, \u201cA Survey on Antispoofing Schemes for Fingerprint Recognition Systems,\u201d ACM Computing Surveys, vol.47, no.2, pp.1-36, 2015. 10.1145\/2617756","DOI":"10.1145\/2617756"},{"key":"7","doi-asserted-by":"publisher","unstructured":"[7] G.E. Hinton and R.R. Salakhutdinov, \u201cReducing the Dimensionality of Data with Neural Networks,\u201d Science, vol.313, no.5786, pp.504-507, 2006. 10.1126\/science.1127647","DOI":"10.1126\/science.1127647"},{"key":"8","unstructured":"[8] I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley,S. Ozair, A. Courville, and Y. Bengio, \u201cGenerative adversarial nets,\u201d Advances in Neural Information Processing Systems, vol.27, pp.2672-2680, 2014."},{"key":"9","unstructured":"[9] N. Babaguchi, \u201cCommunication system for defending against attacks of media clones \u2014 Concept, challenges, and approaches \u2014 ,\u201d IEICE Technical Report, vol.116, no.497, CQ2016-115, pp.25-30, 2017."},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] S. Shiota, F. Villavicencio, J. Yamagishi, N. Ono, I. Echizen, and T. Matsui, \u201cVoice Liveness Detection Algorithms Based on Pop Noise Caused by Human Breath for Automatic Speaker Verification,\u201d Proc. 16th Annual Conf. Int&apos;l Speech Communication Association, pp.239-243, 2015. 10.21437\/odyssey.2016-37","DOI":"10.21437\/Interspeech.2015-92"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] S. Shiota, F. Villavicencio, J. Yamagishi, N. Ono, I. Echizen, and T. Matsui, \u201cVoice Liveness Detection for Speaker Verification Based on a Tandem Single\/Double-Channel Pop Noise Detector,\u201d Proc. Odyssey 2016: The Speaker and Language Recognition Workshop, pp.259-263, 2016. 10.21437\/odyssey.2016-37","DOI":"10.21437\/Odyssey.2016-37"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] V. Conotter, E. Bodnari, G. Boato, and H. Farid, \u201cPhysiologically-Based Detection of Computer Generated Faces in Video,\u201d Proc. 21st IEEE Int&apos;l Conf. Image Processing, pp.248-252, 2014. 10.1109\/icip.2014.7025049","DOI":"10.1109\/ICIP.2014.7025049"},{"key":"13","doi-asserted-by":"crossref","unstructured":"[13] H.H. Nguyen, H.-Q. Nguyen-Son, T.D. Nguyen, and I. Echizen, \u201cDiscriminating Between Computer-Generated Facial Images and Natural Ones Using Smoothness Property and Local Entropy,\u201d Proc. 14th Int&apos;l Workshop on Digital Forensics and Watermarking, pp.39-50, 2016. 10.1007\/978-3-319-31960-5_4","DOI":"10.1007\/978-3-319-31960-5_4"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] Y. Makihara, D. Matovski, M. Nixon, J. Carter, and Y. Yagi, \u201cGait Recognition: Databases, Representations, and Applications,\u201d John Wiley &amp; Sons, Inc., pp.1-15, 2015. 10.1002\/047134608x.w8261","DOI":"10.1002\/047134608X.W8261"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] K. Shiraga, Y. Makihara, D. Muramatsu, T. Echigo, and Y. Yagi, \u201cGEINet: View-Invariant Gait Recognition Using a Convolutional Neural Network,\u201d Proc. 9th IAPR Int&apos;l Conf. Biometrics, pp.1-8, 2016. 10.1109\/icb.2016.7550060","DOI":"10.1109\/ICB.2016.7550060"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] H. 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Derakhshani, S.A.C. Schuckers, L.A. Hornak, and L. O&apos;Gorman, \u201cDetermination of Vitality from a Non-Invasive Biomedical Measurement for Use in Fingerprint Scanner,\u201d Pattern Recognition, vol.36, no.2, pp.383-396, 2003. 10.1016\/s0031-3203(02)00038-9","DOI":"10.1016\/S0031-3203(02)00038-9"},{"key":"26","doi-asserted-by":"publisher","unstructured":"[26] Y.S. Moon, J.S. Chen, K.C. Chan, K. So, and K.C. Woo, \u201cWavelet Based Fingerprint Liveness Detection,\u201d Electronics Letters, vol.41, no.20, pp.1112-1113, 2005. 10.1049\/el:20052577","DOI":"10.1049\/el:20052577"},{"key":"27","unstructured":"[27] A. Hadid, M. Ghahramani, V. Kellokumpu, M. Pietik\u00e4inen, J. Bustard, and M. Nixon, \u201cCan gait biometrics be spoofed?,\u201d Proc. 21st IAPR Int&apos;l Conf. on Pattern Recognition, pp.3280-3283, 2012."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] V. Kellokumpu, G. Zhao, S.Z. Li, M. Pietik\u00e4inen, \u201cDynamic Texture Based Gait Recognition,\u201d Proc. 3rd IAPR Int&apos;l Conf. Biometrics, pp.1000-1009, 2009. 10.1007\/978-3-642-01793-3_101","DOI":"10.1007\/978-3-642-01793-3_101"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] J. Kobayashi, C. Bi, and S. Takahashi, \u201cSophisticated Construction and Search of 2D Motion Graphs for Synthesizing Videos,\u201d Proc. 4th Pacific-Rim Symp. Image and Video Technology, pp.487-494, 2010. 10.1109\/psivt.2010.88","DOI":"10.1109\/PSIVT.2010.88"},{"key":"30","doi-asserted-by":"publisher","unstructured":"[30] N.C. Tang, C.-T. Hsu, M.-F. Weng, T.-Y. Lin, and H.-Y. Liao,\u201cExample-Based Human Motion Extrapolation and Motion Repairing Using Contour Manifold,\u201d IEEE Trans. Multimedia, vol.16, no.1, pp.47-59, 2014. 10.1109\/tmm.2013.2283844","DOI":"10.1109\/TMM.2013.2283844"},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] L. Ma, Q. Sun, S. Georgoulis, L. Van Gool, B. Schiele, and M. 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Kautz, \u201cMoCoGAN: Decomposing Motion and Content for Video Generation,\u201d Proc. 2018 IEEE Conf. Computer Vision and Pattern Recognition, pp.1526-1535, 2018. 10.1109\/cvpr.2018.00165","DOI":"10.1109\/CVPR.2018.00165"},{"key":"35","doi-asserted-by":"crossref","unstructured":"[35] Y. Makihara, H. Mannami, A. Tsuji, M.A. Hossain, K. Sugiura, A. Mori, and Y. Yagi, \u201cThe OU-ISIR Gait Database Comprising the Treadmill Dataset,\u201d IPSJ Trans. Computer Vision and Applications, vol.4, pp.53-62, 2012. 10.2197\/ipsjtcva.4.53","DOI":"10.2197\/ipsjtcva.4.53"},{"key":"36","unstructured":"[36] Y. Hirose, K. Nakamura, N. Nitta, and B. Babaguchi, \u201cGeneration of anonymous gait silhouettes for protecting gait information in videos,\u201d Proc. 2018 IEICE General Conf., D-12-3, p.42, 2018."},{"key":"37","doi-asserted-by":"crossref","unstructured":"[37] R.R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. 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