{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T12:19:48Z","timestamp":1762431588029,"version":"build-2065373602"},"reference-count":53,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2019,1,20]],"date-time":"2019-01-20T00:00:00Z","timestamp":1547942400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2017R1C1B5074062","NRF-2016M3A9E1915855"],"award-info":[{"award-number":["NRF-2017R1C1B5074062","NRF-2016M3A9E1915855"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Face-based biometric recognition systems that can recognize human faces are widely employed in places such as airports, immigration offices, and companies, and applications such as mobile phones. However, the security of this recognition method can be compromised by attackers (unauthorized persons), who might bypass the recognition system using artificial facial images. In addition, most previous studies on face presentation attack detection have only utilized spatial information. To address this problem, we propose a visible-light camera sensor-based presentation attack detection that is based on both spatial and temporal information, using the deep features extracted by a stacked convolutional neural network (CNN)-recurrent neural network (RNN) along with handcrafted features. Through experiments using two public datasets, we demonstrate that the temporal information is sufficient for detecting attacks using face images. In addition, it is established that the handcrafted image features efficiently enhance the detection performance of deep features, and the proposed method outperforms previous methods.<\/jats:p>","DOI":"10.3390\/s19020410","type":"journal-article","created":{"date-parts":[[2019,1,22]],"date-time":"2019-01-22T03:08:22Z","timestamp":1548126502000},"page":"410","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Visible-Light Camera Sensor-Based Presentation Attack Detection for Face Recognition by Combining Spatial and Temporal Information"],"prefix":"10.3390","volume":"19","author":[{"given":"Dat Tien","family":"Nguyen","sequence":"first","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tuyen Danh","family":"Pham","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min Beom","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kang Ryoung","family":"Park","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TCSVT.2003.818349","article-title":"An introduction to biometric recognition","volume":"14","author":"Jain","year":"2004","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Yoon, H.S., Pham, D.T., and Park, K.R. (2017). Spoof detection for finger-vein recognition system using NIR camera. Sensors, 17.","DOI":"10.3390\/s17102261"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"21726","DOI":"10.3390\/s141121726","article-title":"Face recognition system for set-top box-based intelligent TV","volume":"14","author":"Lee","year":"2014","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1166\/asl.2012.2177","article-title":"Combining touched fingerprint and finger-vein of a finger, and its usability evaluation","volume":"5","author":"Nguyen","year":"2012","journal-title":"Adv. Sci. Lett."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16866","DOI":"10.3390\/s150716866","article-title":"Nonintrusive finger-vein recognition system using NIR image sensor and accuracy analyses according to various factors","volume":"15","author":"Pham","year":"2015","journal-title":"Sensors"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"18848","DOI":"10.1109\/ACCESS.2017.2784352","article-title":"Iris recognition with off-the-shelf CNN features: A deep learning perspective","volume":"6","author":"Nguyen","year":"2017","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1016\/j.eswa.2016.01.050","article-title":"An empirical study on iris recognition in a mobile phone","volume":"54","author":"Kim","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_8","first-page":"25","article-title":"Robustness of face recognition to variations of illumination on mobile devices based on SVM","volume":"4","author":"Nam","year":"2010","journal-title":"KSII Trans. Internet Inf. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.3390\/sym7031475","article-title":"Performance enhancement of face recognition in smart TV using symmetrical fuzz-based quality assessment","volume":"7","author":"Kim","year":"2015","journal-title":"Symmetry"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M.A., and Wolf, L. (2014, January 23\u201328). DeepFace: Closing the gap to human-level performance in face verification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.patcog.2017.08.003","article-title":"A survey of local feature methods for 3D face recognition","volume":"72","author":"Soltanpour","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2679","DOI":"10.1109\/TCSVT.2017.2710120","article-title":"Unconstrained face recognition using a set-to-set distance measure on deep learned features","volume":"28","author":"Zhao","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_13","unstructured":"Zhang, Z., Yan, J., Liu, S., Lei, Z., Yi, D., and Li, S.Z. (April, January 29). A face anti-spoofing database with diverse attack. Proceedings of the 5th International Conference on Biometric, New Delhi, India."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Costa-Pazo, A., Bhattacharjee, S., Vazquez-Fernandez, E., and Marcel, S. (2016, January 21\u201323). The replay-mobile face presentation attack database. Proceedings of the International Conference on the Biometrics Special Interest Group, Darmstadt, Germary.","DOI":"10.1109\/BIOSIG.2016.7736936"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.3390\/s150101537","article-title":"Face liveness detection using defocus","volume":"15","author":"Kim","year":"2015","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tan, X., Li, Y., Liu, J., and Jiang, L. (2010, January 5\u201311). Face liveness detection from a single image with sparse low rank bilinear discriminative model. Proceedings of the 11th European Conference on Computer Vision, Crete, Greece.","DOI":"10.1007\/978-3-642-15567-3_37"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Maatta, J., Hadid, A., and Pietikainen, M. (2011, January 11\u201313). Face spoofing detection from single image using micro-texture analysis. Proceedings of the International Joint Conference on Biometric, Washington, DC, USA.","DOI":"10.1109\/IJCB.2011.6117510"},{"key":"ref_18","first-page":"4721849","article-title":"Face spoof attack recognition using discriminative image patches","volume":"2016","author":"Akhtar","year":"2016","journal-title":"J. Electr. Comput. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Boulkenafet, Z., Komulainen, J., and Hadid, A. (2015, January 27\u201330). Face anti-spoofing based on color texture analysis. Proceedings of the IEEE International Conference on Image Processing, Quebec City, QC, Canada.","DOI":"10.1109\/ICIP.2015.7351280"},{"key":"ref_20","first-page":"1397","article-title":"Deep texture features for robust face spoofing detection","volume":"64","author":"Pires","year":"2017","journal-title":"IEEE Trans. Circuits Syst. II-Express"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Galbally, J., and Marcel, S. (2014, January 24\u201328). Face anti-spoofing based on general image quality assessment. Proceedings of the 22nd International Conference on Pattern Recognition, Stockholm, Sweden.","DOI":"10.1109\/ICPR.2014.211"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Benlamoudi, A., Samai, D., Ouafi, A., Bekhouche, S.E., Taleb-Ahmed, A., and Hadid, A. (2015, January 25\u201327). Face spoofing detection using local binary patterns and Fisher score. Proceedings of the 3rd International Conference on Control, Engineering and Information Technology, Tlemcen, Algeria.","DOI":"10.1109\/CEIT.2015.7233145"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Parveen, S., Ahmad, S.M.S., Abbas, N.H., Adnan, W.A.W., Hanafi, M., and Naeem, N. (2016). Face liveness detection using dynamic local ternary pattern (DLTP). Computers, 5.","DOI":"10.3390\/computers5020010"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1186\/1687-5281-2014-2","article-title":"Face liveness detection using dynamic texture","volume":"2014","author":"Komulainen","year":"2014","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_25","unstructured":"Wasnik, P., Raja, K.B., Raghavendra, R., and Busch, C (December, January 28). Presentation attack detection in face biometric systems using raw sensor data from smartphones. Proceedings of the 12th International Conference on Signal Image Technology and Internet-based Systems, Naples, Italy."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1109\/TIFS.2015.2398817","article-title":"Deep representation for iris, face and fingerprint spoofing detection","volume":"10","author":"Menotti","year":"2015","journal-title":"IEEE Trans. Inf. Forensic Secur."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Pham, D.T., Baek, N.R., and Park, K.R. (2018). Combining deep and handcrafted image features for presentation attack detection in face recognition systems using visible-light camera sensors. Sensors, 18.","DOI":"10.3390\/s18030699"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Xu, Z., Li, S., and Deng, W. (2015, January 3\u20136). Learning temporal features using LSTM-CNN architecture for face anti-spoofing. Proceedings of the 3rd Asian Conference on Pattern Recognition, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ACPR.2015.7486482"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1689","DOI":"10.1016\/j.cviu.2013.07.010","article-title":"Face recognition in low resolution thermal images","volume":"117","author":"Mostafa","year":"2013","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Saleb, Y., and Edirisinghe, E. (2016, January 23\u201325). Novel approach to enhance face recognition using depth maps. Proceedings of the International Conference on Systems, Signals and Image Processing, Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2016.7502699"},{"key":"ref_31","unstructured":"(2018, December 10). Dongguk Face Presentation Attack Detection Algorithms by Spatial and Temporal Information (DFPAD-STI). Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kazemi, V., and Sullivan, J. (2014, January 23\u201328). One millisecond face alignment with an ensemble of regression trees. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Robust real-time object detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Qin, H., Yan, J., Li, X., and Hu, X. (2016, January 27\u201330). Joint training of cascaded CNN for face detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.376"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (arXiv, 2016). You only look once: Unified, real-time object detection, arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_36","unstructured":"Simonyan, K., and Zisserman, A. (2013, January 25\u201327). Very deep convolutional neural networks for large-scale image recognition. Proceedings of the International Conference on Learning Representations, Kunming, China."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Nguyen, D.T., Kim, K.W., Hong, H.G., Koo, J.H., Kim, M.C., and Park, K.R. (2017). Gender recognition from human-body images using visible-light and thermal camera videos based on a convolutional neural network for image feature extraction. Sensors, 17.","DOI":"10.3390\/s17030637"},{"key":"ref_40","unstructured":"Salehinejad, H., Sankar, S., Barfett, J., Colak, E., and Valaee, S. (arXiv, 2017). Recent advances in recurrent neural network, arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, D., Ye, M., Li, X., Zhang, F., and Lin, L. (2016, January 19\u201322). Memory-based gait recognition. Proceedings of the British Machine Vision Conference, York, UK.","DOI":"10.5244\/C.30.82"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Zhu, W., Lan, C., Xing, J., Zeng, W., Li, Y., Shen, L., and Xie, X. (2016, January 12\u201317). Co-occurrence feature learning for skeleton based action recognition using regularized deep LSTM networks. Proceedings of the 30th AAAI Conference on Artificial Intelligence, Phoenix, AZ, USA.","DOI":"10.1609\/aaai.v30i1.10451"},{"key":"ref_44","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from over-fitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","article-title":"Multiresolution gray-scale and rotation invariant texture classification with local binary patterns","volume":"24","author":"Ojala","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"11177","DOI":"10.1007\/s11042-015-3052-0","article-title":"Periocular-based biometrics robust to eye rotation based on polar coordinates","volume":"76","author":"Cho","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.eswa.2016.09.024","article-title":"Enhanced age estimation by considering the areas of non-skin and the non-uniform illumination of visible light camera sensor","volume":"66","author":"Nguyen","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1145\/1961189.1961199","article-title":"LIBSVM: A library for support vector machines","volume":"2","author":"Chang","year":"2011","journal-title":"ACM Trans. Intell. Syst. Technol."},{"key":"ref_49","unstructured":"(2019, December 10). Keras Library for Deep Learning. Available online: https:\/\/keras.io\/."},{"key":"ref_50","unstructured":"(2018, December 10). Scikit-Learn Library for Machine Learning. Available online: https:\/\/scikit-learn.org\/stable\/."},{"key":"ref_51","unstructured":"(2018, December 10). NVIDIA TitanX. Available online: https:\/\/www.nvidia.com\/en-us\/geforce\/products\/10series\/titan-x-pascal\/."},{"key":"ref_52","unstructured":"ISO\/IEC JTC1 SC37 Biometrics (2014). ISO\/IEC WD 30107\u20133: 2014 Information Technology\u2014Presentation Attack Detection-Part 3: Testing and Reporting and Classification of Attacks, International Organization for Standardization."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8883","DOI":"10.1007\/s11042-017-4780-0","article-title":"Face presentation attack detection using guided scale texture","volume":"77","author":"Peng","year":"2018","journal-title":"Multimed. Tools Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/410\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:27:29Z","timestamp":1760185649000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/2\/410"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,20]]},"references-count":53,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2019,1]]}},"alternative-id":["s19020410"],"URL":"https:\/\/doi.org\/10.3390\/s19020410","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2019,1,20]]}}}