{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:47:30Z","timestamp":1760240850285,"version":"build-2065373602"},"reference-count":19,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2019,10,3]],"date-time":"2019-10-03T00:00:00Z","timestamp":1570060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Face recognition using a near-infrared (NIR) sensor is widely applied to practical applications such as mobile unlocking or access control. However, unlike RGB sensors, few deep learning approaches have studied NIR face recognition. We conducted comparative experiments for the application of deep learning to NIR face recognition. To accomplish this, we gathered five public databases and trained two deep learning architectures. In our experiments, we found that simple architecture could have a competitive performance on the NIR face databases that are mostly composed of frontal face images. Furthermore, we propose a data augmentation method to train the architectures to improve recognition of users who wear glasses. With this augmented training set, the recognition rate for users who wear glasses increased by up to 16%. This result implies that the recognition of those who wear glasses can be overcome using this simple method without constructing an additional training set. Furthermore, the model that uses augmented data has symmetry with those trained with real glasses-wearing data regarding the recognition of people who wear glasses.<\/jats:p>","DOI":"10.3390\/sym11101234","type":"journal-article","created":{"date-parts":[[2019,10,4]],"date-time":"2019-10-04T04:12:52Z","timestamp":1570162372000},"page":"1234","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["NIR Reflection Augmentation for DeepLearning-Based NIR Face Recognition"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1477-3579","authenticated-orcid":false,"given":"Hoon","family":"Jo","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0320-1409","authenticated-orcid":false,"given":"Whoi-Yul","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Engineering, Hanyang University, Seoul 04763, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 24\u201327). DeepFace: Closing the Gap to Human-Level Performance in Face Verification. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.220"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TPAMI.2007.1014","article-title":"Illumination invariant face recognition using near-infrared images","volume":"29","author":"Li","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pan, K., Liao, S., Zhang, Z., Li, S.Z., and Zhang, P. (2007, January 17\u201322). Part-based Face Recognition Using Near Infrared Images. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383459"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.-Y., and Satoh, S. (2019, January 15\u201321). Learning to Reduce Dual-Level Discrepancy for Infrared-Visible Person Re-Identification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00071"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Iranmanesh, S.M., Dabouei, A., Kazemi, H., and Nasrabadi, N.M. (2018, January 20\u201323). Deep cross polarimetric thermal-to-visible face recognition. Proceedings of the 2018 International Conference on Biometrics (ICB 2018), Gold Coast, Australia.","DOI":"10.1109\/ICB2018.2018.00034"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"He, R., Cao, J., Song, L., Sun, Z., and Tan, T. (2019). Cross-spectral Face Completion for NIR-VIS Heterogeneous Face Recognition. arXiv.","DOI":"10.1109\/TPAMI.2019.2961900"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lezama, J., Qiu, Q., and Sapiro, G. (2017, January 21\u201326). Not afraid of the dark: NIR-VIS face recognition via cross-spectral hallucination and low-rank embedding. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.720"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Peng, M., Wang, C., Chen, T., and Liu, G. (2016). NIRFaceNet: A convolutional neural network for near-infrared face identification. Information, 7.","DOI":"10.3390\/info7040061"},{"key":"ref_10","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Parkhi, O.M., Vedaldi, A., and Zisserman, A. (2015, January 7\u201310). Deep Face Recognition. Proceedings of the British Machine Vision Conference 2015, British Machine Vision Association, Swansea, UK.","DOI":"10.5244\/C.29.41"},{"key":"ref_12","first-page":"11","article-title":"Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning","volume":"42","author":"Szegedy","year":"2016","journal-title":"Pattern Recognit. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (July, January 26). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_14","first-page":"1062","article-title":"Learning Face Representation from Scratch","volume":"53","author":"Yi","year":"2014","journal-title":"J. Struct. Chem."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Li, S.Z., Yi, D., Lei, Z., and Liao, S. (2013, January 23\u201328). The CASIA NIR-VIS 2.0 Face Database. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA.","DOI":"10.1109\/CVPRW.2013.59"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1016\/j.patrec.2010.07.006","article-title":"Directional binary code with application to PolyU near-infrared face database","volume":"31","author":"Zhang","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bernhard, J., Barr, J., Bowyer, K.W., and Flynn, P. (2015, January 8\u201311). Near-IR to visible light face matching: Effectiveness of pre-processing options for commercial matchers. Proceedings of the 2015 IEEE 7th International Conference on Biometrics Theory, Applications and Systems, BTAS 2015, Arlington, VA, USA.","DOI":"10.1109\/BTAS.2015.7358780"},{"key":"ref_18","unstructured":"Perez, L., and Wang, J. (2017). The Effectiveness of Data Augmentation in Image Classification using Deep Learning. arXiv."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","article-title":"Joint Face Detection and Alignment Using Multitask Cascaded Convolutional Networks","volume":"23","author":"Zhang","year":"2016","journal-title":"IEEE Signal Process. Lett."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1234\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:16Z","timestamp":1760189236000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/11\/10\/1234"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,3]]},"references-count":19,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["sym11101234"],"URL":"https:\/\/doi.org\/10.3390\/sym11101234","relation":{},"ISSN":["2073-8994"],"issn-type":[{"type":"electronic","value":"2073-8994"}],"subject":[],"published":{"date-parts":[[2019,10,3]]}}}