{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T05:55:53Z","timestamp":1780466153685,"version":"3.54.1"},"reference-count":52,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,10,1]],"date-time":"2017-10-01T00:00:00Z","timestamp":1506816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.<\/jats:p>","DOI":"10.3390\/s17102261","type":"journal-article","created":{"date-parts":[[2017,10,2]],"date-time":"2017-10-02T13:10:05Z","timestamp":1506949805000},"page":"2261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Spoof Detection for Finger-Vein Recognition System Using NIR Camera"],"prefix":"10.3390","volume":"17","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":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyo Sik","family":"Yoon","sequence":"additional","affiliation":[{"name":"Division of Electronics and Electrical Engineering, Dongguk University, 30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]},{"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":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,10,1]]},"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":"2014","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1109\/THMS.2015.2412944","article-title":"Biometric-enabled authentication machines: A survey of open-set real-world applications","volume":"46","author":"Eastwood","year":"2016","journal-title":"IEEE T. Hum. Mach. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"14615","DOI":"10.3390\/s150614615","article-title":"Fingerprint liveness detection in the presence of capable intruders","volume":"15","author":"Sequeira","year":"2015","journal-title":"Sensors"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1049\/iet-bmt.2013.0020","article-title":"Presentation attack detection method for fingerprint recognition systems: A survey","volume":"3","author":"Soudedik","year":"2014","journal-title":"IET Biom."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"447","DOI":"10.1016\/j.procs.2015.08.061","article-title":"A minutiae count based method for fake fingerprint detection","volume":"58","author":"Abhishek","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","first-page":"12804","DOI":"10.3390\/s131012804","article-title":"3D multi-spectrum sensor system with face recognition","volume":"13","author":"Kim","year":"2013","journal-title":"Sensors"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"17089","DOI":"10.3390\/s150717089","article-title":"Intensity variation normalization for finger vein recognition using guided filter based on single scale retinex","volume":"15","author":"Xie","year":"2015","journal-title":"Sensors"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jnca.2009.12.006","article-title":"Finger vein recognition with manifold learning","volume":"33","author":"Liu","year":"2010","journal-title":"J. Netw. Comput. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"31339","DOI":"10.3390\/s151229856","article-title":"Bimodal biometric verification using the fusion of palmprint and infrared palm-dorsum vein images","volume":"15","author":"Lin","year":"2015","journal-title":"Sensors"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13225","DOI":"10.1016\/j.eswa.2012.05.079","article-title":"Palm vein recognition using adaptive Gabor filter","volume":"39","author":"Han","year":"2012","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TIP.2016.2616281","article-title":"Toward more accurate iris recognition using cross-spectral matching","volume":"26","author":"Nalla","year":"2017","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/TIFS.2016.2606083","article-title":"Optimal generation of iris codes for iris recognition","volume":"12","author":"Hu","year":"2017","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.patrec.2016.02.001","article-title":"Iris recognition through machine learning techniques: A survey","volume":"82","author":"Marsico","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5772\/51664","article-title":"New fuzzy-based retinex method for illumination normalization of face recognition","volume":"9","author":"Nam","year":"2012","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1109\/TIP.2012.2220373","article-title":"Adaptive fingerprint image enhancement with emphasis on preprocessing of data","volume":"22","author":"Bartunek","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"1818","DOI":"10.1109\/TIFS.2016.2555286","article-title":"Face spoofing detection using colour texture analysis","volume":"11","author":"Boulkenafet","year":"2016","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1530","DOI":"10.1109\/ACCESS.2014.2381273","article-title":"Biometric anti-spoofing methods: A survey in face recognition","volume":"2","author":"Galbally","year":"2014","journal-title":"IEEE Access"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Lee, E.C., Ko, Y.J., and Park, K.R. (2009). Fake iris detection method using Purkinje images based on gaze position. Opt. Eng., 47.","DOI":"10.1117\/1.2947582"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"710","DOI":"10.1109\/TIP.2013.2292332","article-title":"Image quality assessment for fake biometric detection: Application to iris, fingerprint and face recognition","volume":"23","author":"Galbally","year":"2014","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tome, P., Raghavendra, R., Busch, C., Tirunagari, S., Poh, N., Shekar, B.H., Gragnaniello, D., Sansone, C., Verdoliva, L., and Marcel, S. (2015, January 19\u201322). The 1st competition on counter measures to finger vein spoofing attacks. Proceedings of the International Conference on Biometrics, Phuket, Thailand.","DOI":"10.1109\/ICB.2015.7139067"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1401","DOI":"10.1016\/j.dsp.2013.04.001","article-title":"Fake finger-vein image detection based on fourier and wavelet transforms","volume":"23","author":"Nguyen","year":"2013","journal-title":"Digit. Signal Process."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kocher, D., Schwarz, S., and Uhl, A. (2016, January 21\u201323). Empirical evaluation of LBP-extension features for finger vein spoofing detection. Proceedings of the International Conference of the Biometrics Special Interest Group, Darmstadt, Germany.","DOI":"10.1109\/BIOSIG.2016.7736921"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tirunagari, S., Poh, N., Bober, M., and Windridge, D. (2015, January 16\u201319). Windowed DMD as a micro-texture descriptor for finger vein counter-spoofing in biometrics. Proceedings of the IEEE International Workshop on Information Forensics and Security, Rome, Italy.","DOI":"10.1109\/WIFS.2015.7368599"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Raghavendra, R., and Busch, C. (2015, January 23\u201327). Presentation attack detection algorithms for finger vein biometrics: A comprehensive study. Proceedings of the 11th International Conference on Signal-Image Technology and Internet-based Systems, Bangkok, Thailand.","DOI":"10.1109\/SITIS.2015.74"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qin, B., Pan, J.F., Cao, G.Z., and Du, G.G. (2009, January 11\u201314). The anti-spoofing study of vein identification system. Proceedings of the International Conference on Computational Intelligence and Security, Beijing, China.","DOI":"10.1109\/CIS.2009.144"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1049\/iet-cvi.2009.0081","article-title":"Multi-model biometric method based on vein and geometry of a single finger","volume":"4","author":"Kang","year":"2010","journal-title":"IET Comput. Vis."},{"key":"ref_30","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_31","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Advances in Neural Information Processing Systems, Lake Tahoe, NV, USA."},{"key":"ref_32","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_33","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Li, W., Zhao, R., Xiao, T., and Wang, X. (2014, January 23\u201328). Deepreid: Deep filter pairing neural network for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.27"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cheng, D., Gong, Y., Zhou, S., Wang, J., and Zheng, N. (2016, January 27\u201330). Person re-identification by multi-channel parts-based CNN with improved triplet loss function. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.149"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ahmed, E., Jones, M., and Marks, T.K. (2015, January 7\u201312). An improved deep learning architecture for person re-identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299016"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Zhang, X., Sugano, Y., Fritz, M., and Bulling, A. (2015, January 7\u201312). Appearance-based gaze estimation in the wild. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7299081"},{"key":"ref_38","unstructured":"Gurghian, A., Koduri, T., Bailur, S.V., Carey, K.J., and Murali, V.N. (July, January 26). DeepLanes: End-to-end lane position estimation using deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Las Vegas, NV, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Krafka, K., Khosla, A., Kellnhofer, P., Kannan, H., Bhandarkar, S., Matusik, W., and Torralba, A. (2016, January 27\u201330). Eye tracking for everyone. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.239"},{"key":"ref_40","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_41","first-page":"1929","article-title":"Dropout: A simple way to prevent neural networks from overfitting","volume":"15","author":"Srivastava","year":"2014","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_43","unstructured":"George, D., Shen, H., and Huerta, E.A. (2017, September 27). Deep transfer learning: A new deep learning glitch classification method for advanced LIGO. Available online: http:\/\/ arXiv.org\/abs\/1706.07446."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.patcog.2017.05.025","article-title":"Handcrafted vs. non-handcrafted features for computer vision classification","volume":"71","author":"Nanni","year":"2017","journal-title":"Pattern Recognit."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.patrec.2016.11.011","article-title":"How could a subcellular image, or a painting by Van Gogh, be similar to a great white shark or to a pizza?","volume":"85","author":"Nanni","year":"2017","journal-title":"Pattern Recognit. Lett."},{"key":"ref_46","unstructured":"Ravishankar, H., Sudhakar, P., Venkataramani, R., Thiruvenkadam, S., Annangi, P., Babu, N., and Vaidya, V. (2017, September 27). Understanding the mechanisms of deep transfer learning for medical images. Available online: https:\/\/arxiv.org\/abs\/1704.06040."},{"key":"ref_47","unstructured":"(2017, April 30). ISPR Database (Real and Presentation Attack Finger-Vein Images) and Algorithm Including CNN Model. Available online: http:\/\/dm.dgu.edu\/link.html."},{"key":"ref_48","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_49","unstructured":"(2017, April 30). MATLAB Convolutional Neural Networks (CNN) Toolbox. Available online: https:\/\/www.mathworks.com\/help\/nnet\/convolutional-neural-networks.html?s_tid=gn_loc_drop."},{"key":"ref_50","unstructured":"(2014). ISO\/IEC JTC1 SC37 Biometrics. 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_51","doi-asserted-by":"crossref","unstructured":"Tome, P., and Marcel, S. (2015, January 19\u201322). On the vulnerability of palm vein recognition to spoofing attacks. Proceedings of the International Conference on Biometrics, Phuket, Thailand.","DOI":"10.1109\/ICB.2015.7139056"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1049\/iet-bmt.2016.0085","article-title":"Palm-vein recognition scheme based on an adaptive Gabor filter","volume":"6","author":"Ma","year":"2017","journal-title":"IET Biom."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2261\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:46:27Z","timestamp":1760208387000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/10\/2261"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,1]]},"references-count":52,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2017,10]]}},"alternative-id":["s17102261"],"URL":"https:\/\/doi.org\/10.3390\/s17102261","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,10,1]]}}}