{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:33:38Z","timestamp":1774539218163,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2016,10,27]],"date-time":"2016-10-27T00:00:00Z","timestamp":1477526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61301297"],"award-info":[{"award-number":["61301297"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61472330"],"award-info":[{"award-number":["61472330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XDJK2013C124"],"award-info":[{"award-number":["XDJK2013C124"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Southwest University Doctoral Foundation","award":["SWU115093"],"award-info":[{"award-number":["SWU115093"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Near-infrared (NIR) face recognition has attracted increasing attention because of its advantage of illumination invariance. However, traditional face recognition methods based on NIR are designed for and tested in cooperative-user applications. In this paper, we present a convolutional neural network (CNN) for NIR face recognition (specifically face identification) in non-cooperative-user applications. The proposed NIRFaceNet is modified from GoogLeNet, but has a more compact structure designed specifically for the Chinese Academy of Sciences Institute of Automation (CASIA) NIR database and can achieve higher identification rates with less training time and less processing time. The experimental results demonstrate that NIRFaceNet has an overall advantage compared to other methods in the NIR face recognition domain when image blur and noise are present. The performance suggests that the proposed NIRFaceNet method may be more suitable for non-cooperative-user applications.<\/jats:p>","DOI":"10.3390\/info7040061","type":"journal-article","created":{"date-parts":[[2016,10,27]],"date-time":"2016-10-27T10:17:52Z","timestamp":1477563472000},"page":"61","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["NIRFaceNet: A Convolutional Neural Network for Near-Infrared Face Identification"],"prefix":"10.3390","volume":"7","author":[{"given":"Min","family":"Peng","sequence":"first","affiliation":[{"name":"School of Electronic and Information Engineering, Shouthwest University, Chongqing 400715, China"},{"name":"Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chongyang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Shouthwest University, Chongqing 400715, China"},{"name":"Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3805-4138","authenticated-orcid":false,"given":"Tong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Shouthwest University, Chongqing 400715, China"},{"name":"Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyuan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Shouthwest University, Chongqing 400715, China"},{"name":"Chongqing Key Laboratory of Nonlinear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2016,10,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wolf, L., Hassner, T., and Maoz, I. (2011, January 20\u201325). Face recognition in unconstrained videos with matched background similarity. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995566"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1801","DOI":"10.1016\/j.patcog.2009.11.023","article-title":"Thermal and reflectance based personal identification methodology under variable illumination","volume":"43","author":"Hammoud","year":"2010","journal-title":"Pattern Recognit."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"210","DOI":"10.1109\/TPAMI.2008.79","article-title":"Robust face recognition via sparse representation","volume":"31","author":"Wright","year":"2009","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Arandjelovi\u0107, O., and Cipolla, R. (2006, January 20\u201324). Face set classification using maximally probable mutual modes. Proceedings of the 18 International Conference on Pattern Recognition, Hong Kong, China.","DOI":"10.1109\/ICPR.2006.535"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Yin, Q., Tang, X., and Sun, J. (2011, January 20\u201325). An associate-predict model for face recognition. Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, USA.","DOI":"10.1109\/CVPR.2011.5995494"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1109\/34.598229","article-title":"Face recognition: the problem of compensating for changes in illumination direction","volume":"19","author":"Adini","year":"1997","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"371","DOI":"10.3758\/BF03330623","article-title":"Illumination effects in face recognition","volume":"26","author":"Braje","year":"1998","journal-title":"Psychobiology"},{"key":"ref_8","unstructured":"Phillips, P.J., Flynn, P.J., Scruggs, T., Bowyer, K.W., Chang, J., Hoffman, K., Marques, J., Min, J., and Worek, W. (2005, January 20\u201325). Overview of the face recognition grand challenge. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1049\/iet-cvi.2014.0086","article-title":"Addressing the illumination challenge in two-dimensional face recognition: A survey","volume":"9","author":"Kakadiaris","year":"2015","journal-title":"IET Comput. Vis."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Petrovska-Delacr\u00e9taz, D., Dorizzi, B., and Chollet, G. (2009). Guide to Biometric Reference Systems and Performance Evaluation, Springer.","DOI":"10.1007\/978-1-84800-292-0"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2270","DOI":"10.1109\/TPAMI.2013.48","article-title":"3D face recognition under expressions, occlusions, and pose variations","volume":"35","author":"Drira","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.ins.2015.03.063","article-title":"Bayesian multi-distribution-based discriminative feature extraction for 3D face recognition","volume":"320","author":"Liang","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_13","unstructured":"Shen, L., and Zheng, S. (2012, January 11\u201315). Hyperspectral face recognition using 3d Gabor wavelets. Proceedings of the 21st International Conference on Pattern Recognition, Tsukuba, Japan."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1109\/TIP.2015.2393057","article-title":"Hyperspectral face recognition with spatiospectral information fusion and PLS regression","volume":"24","author":"Uzair","year":"2015","journal-title":"IEEE Trans. Image Process."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Bourlai, T. (2016). Face Recognition across the Imaging Spectrum, Springer.","DOI":"10.1007\/978-3-319-28501-6"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2445","DOI":"10.1016\/j.patcog.2012.01.001","article-title":"A comparative study of thermal face recognition methods in unconstrained environments","volume":"45","author":"Hermosilla","year":"2012","journal-title":"Pattern Recognit."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2807","DOI":"10.1016\/j.patcog.2014.03.015","article-title":"Infrared face recognition: A comprehensive review of methodologies and databases","volume":"47","author":"Ghiass","year":"2014","journal-title":"Pattern Recognit."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Chora\u015b, R.S. (2016). Image Processing and Communications Challenges 7, Springer.","DOI":"10.1007\/978-3-319-23814-2"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, B.Y.L., Mian, A.S., Liu, W., and Krishna, A. (2013, January 15\u201317). Using kinect for face recognition under varying poses, expressions, illumination and disguise. Proceedings of 2013 IEEE Workshop on Applications of Computer Vision, Clearwater Beach, FL, USA.","DOI":"10.1109\/WACV.2013.6475017"},{"key":"ref_20","unstructured":"Goswami, G., Bharadwaj, S., Vatsa, M., and Singh, R. (October, January 29). On RGB-D face recognition using Kinect. Proceedings of IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems, Arlington, VA, USA."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1007\/s10044-015-0456-4","article-title":"Face recognition based on Kinect","volume":"19","author":"Li","year":"2016","journal-title":"Pattern Anal. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bourlai, T. (2016). Face Recognition across the Imaging Spectrum, Springer.","DOI":"10.1007\/978-3-319-28501-6"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Li, S.Z., and Jain, A. (2015). Encyclopedia of Biometrics, Springer.","DOI":"10.1007\/978-1-4899-7488-4"},{"key":"ref_25","unstructured":"Farokhi, S., Shamsuddin, S.M., Sheikh, U.U., and Flusser, J. (2014). Innovation Excellence towards Humanistic Technology, Springer."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"23","DOI":"10.11113\/jt.v70.2459","article-title":"Evaluating feature extractors and dimension reduction methods for near infrared face recognition systems","volume":"70","author":"Farokhi","year":"2014","journal-title":"Jurnal Teknologi"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.dsp.2014.04.008","article-title":"Near infrared face recognition by combining Zernike moments and undecimated discrete wavelet transform","volume":"31","author":"Farokhi","year":"2014","journal-title":"Digit. Signal Process."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ins.2015.04.030","article-title":"Near infrared face recognition using Zernike moments and Hermite kernels","volume":"316","author":"Farokhi","year":"2015","journal-title":"Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Taigman, Y., Yang, M., Ranzato, M., and Wolf, L. (2014, January 23\u201328). 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_30","doi-asserted-by":"crossref","unstructured":"Sun, Y., Wang, X., and Tang, X. (2014, January 23\u201328). Deep learning face representation from predicting 10,000 Classes. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.244"},{"key":"ref_31","unstructured":"Yi, D., Lei, Z., Liao, S., and Li, S.Z. (2014). Learning face representation from scratch."},{"key":"ref_32","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"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":"Bottou","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_34","unstructured":"Krizhevsky, A., Sutskever, I., and Hinton, G.E. (2012, January 3\u20138). ImageNet classification with deep convolutional neural networks. Proceedings of the Twenty-sixth Annual Conference on Neural Information Processing Systems (NIPS), Stateline, NV, USA."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Collobert, R., and Weston, J. (2008, January 5\u20139). A unified architecture for natural language processing: Deep neural networks with multitask learning. Proceedings of the 25th international conference on Machine learning, Helsinki, Finland.","DOI":"10.1145\/1390156.1390177"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Abdel-Hamid, O., Mohamed, A.-R., Jiang, H., and Penn, G. (2012, January 25\u201330). Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition. Proceedings of 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Kyoto, Japan.","DOI":"10.1109\/ICASSP.2012.6288864"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.R., and Hinton, G. (2013, January 26\u201331). Speech recognition with deep recurrent neural networks. Proceeding of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Vancouver, BC, Canada.","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"ref_39","unstructured":"Nair, V., and Hinton, G.E. (2010, January 21\u201324). Rectified linear units improve restricted Boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Israel."},{"key":"ref_40","unstructured":"Bouchard, G. (2007, January 7\u20138). Efficient bounds for the softmax function and applications to approximate inference in hybrid models. Proceedings of NIPS 2007 workshop for approximate Bayesian inference in continuous\/hybrid systems, Whistler, BC, Canada."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1429","DOI":"10.1016\/S0893-6080(03)00138-2","article-title":"The general inefficiency of batch training for gradient descent learning","volume":"16","author":"Wilson","year":"2003","journal-title":"Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"McDonnell, M.D., Tissera, M.D., Vladusich, T., van Schaik, A., and Tapson, J. (2015). Fast, simple and accurate handwritten digit classification by training shallow neural network classifiers with the \u201cExtreme Learning Machine\u201d algorithm. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0134254"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Hu, G., Yang, Y., Yi, D., Kittler, J., Christmas, W., Li, S.Z., and Hospedales, T. (2015, January 13\u201316). When face recognition meets with deep learning: An evaluation of convolutional neural networks for face recognition. Proceedings of the 2015 IEEE International Conference on Computer Vision Workshops, Santiago, Chile.","DOI":"10.1109\/ICCVW.2015.58"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2247","DOI":"10.1109\/TITS.2015.2402438","article-title":"Vehicle type classification using a semi supervised convolutional neural network","volume":"16","author":"Dong","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_45","first-page":"105","article-title":"Robust Convolutional Neural Networks for Image Recognition","volume":"6","author":"Hayder","year":"2015","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2037","DOI":"10.1109\/TPAMI.2006.244","article-title":"Face description with local binary patterns: Application to face recognition","volume":"28","author":"Ahonen","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1016\/j.jvcir.2006.11.004","article-title":"ENCARA2: Real-time detection of multiple faces at different resolutions in video streams","volume":"18","author":"Guerra","year":"2007","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., and Darrell, T. (2014, January 3\u20137). Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, Orlando, FL, USA.","DOI":"10.1145\/2647868.2654889"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"4049","DOI":"10.1109\/TIP.2013.2268976","article-title":"Noise-resistant local binary pattern with an embedded error-correction mechanism","volume":"22","author":"Ren","year":"2013","journal-title":"IEEE Trans. Image Process."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kemelmacher-Shlizerman, I., Seitz, S., Miller, D., and Brossard, E. (2016). The MegaFace benchmark: 1 million faces for recognition at scale.","DOI":"10.1109\/CVPR.2016.527"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/7\/4\/61\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T19:34:07Z","timestamp":1760211247000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/7\/4\/61"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,10,27]]},"references-count":51,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2016,12]]}},"alternative-id":["info7040061"],"URL":"https:\/\/doi.org\/10.3390\/info7040061","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,10,27]]}}}