{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:17:14Z","timestamp":1743099434914,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030821982"},{"type":"electronic","value":"9783030821999"}],"license":[{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,7]],"date-time":"2021-08-07T00:00:00Z","timestamp":1628294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-82199-9_56","type":"book-chapter","created":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T09:02:45Z","timestamp":1636534965000},"page":"830-844","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["More Than Just an Auxiliary Loss: Anti-spoofing Backbone Training via Adversarial Pseudo-depth Generation"],"prefix":"10.1007","author":[{"given":"Chang Keun","family":"Paik","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Naeun","family":"Ko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjoon","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,7]]},"reference":[{"key":"56_CR1","doi-asserted-by":"crossref","unstructured":"Agarwal, A., Singh, R., Vatsa, M.: Face anti-spoofing using haralick features. In: 2016 IEEE 8th International Conference on Biometrics Theory, Applications and Systems (BTAS), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/BTAS.2016.7791171"},{"key":"56_CR2","doi-asserted-by":"crossref","unstructured":"Atoum, Y., Liu, Y., Jourabloo, A., Liu, X.: Face anti-spoofing using patch and depth-based CNNs. In: 2017 IEEE International Joint Conference on Biometrics (IJCB), pp. 319\u2013328. IEEE (2017)","DOI":"10.1109\/BTAS.2017.8272713"},{"key":"56_CR3","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Hadid, A.: Face anti-spoofing based on color texture analysis. In: 2015 IEEE International Conference on Image Processing (ICIP), pp. 2636\u20132640. IEEE (2015)","DOI":"10.1109\/ICIP.2015.7351280"},{"key":"56_CR4","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., Li, L., Feng, X., Hadid, A.: Oulu-npu: a mobile face presentation attack database with real-world variations. In: 2017 12th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2017), pp. 612\u2013618. IEEE (2017)","DOI":"10.1109\/FG.2017.77"},{"key":"56_CR5","unstructured":"Chingovska, I., Anjos, A., Marcel, S.:. On the effectiveness of local binary patterns in face anti-spoofing. In: 2012 BIOSIG-Proceedings of the International Conference of Biometrics Special Interest Group (BIOSIG), pp. 1\u20137. IEEE (2012)"},{"key":"56_CR6","doi-asserted-by":"crossref","unstructured":"da\u00a0Silva\u00a0Pinto, A., Pedrini, H., Schwartz, W., Rocha, A.: Video-based face spoofing detection through visual rhythm analysis. In: 2012 25th SIBGRAPI Conference on Graphics, Patterns and Images, pp. 221\u2013228. IEEE (2012)","DOI":"10.1109\/SIBGRAPI.2012.38"},{"key":"56_CR7","doi-asserted-by":"crossref","unstructured":"de Freitas Pereira, T., Anjos, A., De Martino, J.M., Marcel, S.: Can face anti-spoofing countermeasures work in a real world scenario? In: 2013 International Conference on Biometrics (ICB), pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/ICB.2013.6612981"},{"key":"56_CR8","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"56_CR9","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Zhou, Y., Yu, J., Kotsia, I., Zafeiriou, S.: Retinaface: single-stage dense face localisation in the wild. arXiv preprint arXiv:1905.00641 (2019)","DOI":"10.1109\/CVPR42600.2020.00525"},{"key":"56_CR10","doi-asserted-by":"crossref","unstructured":"Feng, Y., Wu, F., Shao, X., Wang, Y., Zhou, X.: Joint 3D face reconstruction and dense alignment with position map regression network. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 534\u2013551 (2018)","DOI":"10.1007\/978-3-030-01264-9_33"},{"key":"56_CR11","doi-asserted-by":"crossref","unstructured":"Gan, J., Li, S., Zhai, Y., Liu, C.: 3D convolutional neural network based on face anti-spoofing. In: 2017 2nd International Conference on Multimedia and Image Processing (ICMIP), pp. 1\u20135. IEEE (2017)","DOI":"10.1109\/ICMIP.2017.9"},{"key":"56_CR12","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"56_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026\u20131034 (2015)","DOI":"10.1109\/ICCV.2015.123"},{"key":"56_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"56_CR15","doi-asserted-by":"crossref","unstructured":"Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125\u20131134 (2017)","DOI":"10.1109\/CVPR.2017.632"},{"key":"56_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"56_CR17","doi-asserted-by":"crossref","unstructured":"Komulainen, J., Hadid, A., Pietik\u00e4inen, M.: Context based face anti-spoofing. In: 2013 IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1\u20138. IEEE (2013)","DOI":"10.1109\/BTAS.2013.6712690"},{"key":"56_CR18","doi-asserted-by":"crossref","unstructured":"Li, H., Li, W., Cao, H., Wang, S., Huang, F., Kot, A.C.: Unsupervised domain adaptation for face anti-spoofing. IEEE Trans. Inf. Forensics Security 13(7), 1794\u20131809 (2018)","DOI":"10.1109\/TIFS.2018.2801312"},{"key":"56_CR19","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, Y., Tan, T., Jain, A.K.: Live face detection based on the analysis of fourier spectra. In: Biometric Technology for Human Identification, vol. 5404, pp. 296\u2013303. International Society for Optics and Photonics (2004)","DOI":"10.1117\/12.541955"},{"key":"56_CR20","doi-asserted-by":"crossref","unstructured":"Li, L., Feng, X., Boulkenafet, Z., Xia, Z., Li, M., Hadid, A.: An original face anti-spoofing approach using partial convolutional neural network. In: 2016 Sixth International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 1\u20136. IEEE (2016)","DOI":"10.1109\/IPTA.2016.7821013"},{"key":"56_CR21","doi-asserted-by":"crossref","unstructured":"Li, X., Komulainen, J., Zhao, G., Yuen, P.-C., Pietik\u00e4inen, M.: Generalized face anti-spoofing by detecting pulse from face videos. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 4244\u20134249. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7900300"},{"key":"56_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Jourabloo, A., Liu, X.: Learning deep models for face anti-spoofing: binary or auxiliary supervision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Jun 2018)","DOI":"10.1109\/CVPR.2018.00048"},{"key":"56_CR23","doi-asserted-by":"crossref","unstructured":"M\u00e4\u00e4tt\u00e4, J., Hadid, A., Pietik\u00e4inen, M.: Face spoofing detection from single images using micro-texture analysis. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1\u20137. IEEE (2011)","DOI":"10.1109\/IJCB.2011.6117510"},{"key":"56_CR24","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier gans. In: International Conference on Machine Learning, pp. 2642\u20132651 (2017)"},{"key":"56_CR25","doi-asserted-by":"crossref","unstructured":"Pan, G., Sun, L., Wu, Z., Lao, S.: Eyeblink-based anti-spoofing in face recognition from a generic webcamera. In: 2007 IEEE 11th International Conference on Computer Vision, pp. 1\u20138. IEEE (2007)","DOI":"10.1109\/ICCV.2007.4409068"},{"key":"56_CR26","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d$$^\\prime $$Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019)"},{"key":"56_CR27","doi-asserted-by":"crossref","unstructured":"Schwartz, W.R., Rocha, A., Pedrini, H.: Face spoofing detection through partial least squares and low-level descriptors. In: 2011 International Joint Conference on Biometrics (IJCB), pp. 1\u20138. IEEE (2011)","DOI":"10.1109\/IJCB.2011.6117592"},{"key":"56_CR28","doi-asserted-by":"crossref","unstructured":"Siddiqui, T.A., et al.: Face anti-spoofing with multifeature videolet aggregation. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 1035\u20131040. IEEE (2016)","DOI":"10.1109\/ICPR.2016.7899772"},{"key":"56_CR29","doi-asserted-by":"crossref","unstructured":"Tirunagari, S., Poh, N., Windridge, D., Iorliam, A., Suki, N., Ho, A.T.S.: Detection of face spoofing using visual dynamics. IEEE Trans. Inf. Forensics Security 10(4), 762\u2013777 (2015)","DOI":"10.1109\/TIFS.2015.2406533"},{"key":"56_CR30","doi-asserted-by":"crossref","unstructured":"Wang, Z., et al.: Deep spatial gradient and temporal depth learning for face anti-spoofing. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 5042\u20135051 (2020)","DOI":"10.1109\/CVPR42600.2020.00509"},{"key":"56_CR31","doi-asserted-by":"crossref","unstructured":"Xu, Z., Li, S., Deng, W.: Learning temporal features using LSTM-CNN architecture for face anti-spoofing. In: 2015 3rd IAPR Asian Conference on Pattern Recognition (ACPR), pp. 141\u2013145. IEEE (2015)","DOI":"10.1109\/ACPR.2015.7486482"},{"key":"56_CR32","unstructured":"Yang, J., Lei, Z., Li, S.Z.: Learn convolutional neural network for face anti-spoofing. arXiv preprint arXiv:1408.5601 (2014)"},{"key":"56_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, P., et al.: Feathernets: convolutional neural networks as light as feather for face anti-spoofing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (2019)","DOI":"10.1109\/CVPRW.2019.00199"},{"key":"56_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: Celeba-spoof: large-scale face anti-spoofing dataset with rich annotations. arXiv preprint arXiv:2007.12342 (2020)","DOI":"10.1007\/978-3-030-58610-2_5"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-82199-9_56","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T21:46:05Z","timestamp":1726091165000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-82199-9_56"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,7]]},"ISBN":["9783030821982","9783030821999"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-82199-9_56","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2021,8,7]]},"assertion":[{"value":"7 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IntelliSys","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Proceedings of SAI Intelligent Systems Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Amsterdam","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"intellisys2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/saiconference.com\/IntelliSys","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}