{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T00:31:57Z","timestamp":1760229117077,"version":"build-2065373602"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T00:00:00Z","timestamp":1654041600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Military Academy Research Center (CINAMIL) under the project Multi-Spectral Facial Recognition","doi-asserted-by":"publisher","award":["UID\/FIS\/04559\/2019","PTDC\/EEI-ROB\/1155\/2020","UIDB\/50009\/2020"],"award-info":[{"award-number":["UID\/FIS\/04559\/2019","PTDC\/EEI-ROB\/1155\/2020","UIDB\/50009\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This work proposes a multi-spectral face recognition system in an uncontrolled environment, aiming to identify or authenticate identities (people) through their facial images. Face recognition systems in uncontrolled environments have shown impressive performance improvements over recent decades. However, most are limited to the use of a single spectral band in the visible spectrum. The use of multi-spectral images makes it possible to collect information that is not obtainable in the visible spectrum when certain occlusions exist (e.g., fog or plastic materials) and in low- or no-light environments. The proposed work uses the scores obtained by face recognition systems in different spectral bands to make a joint final decision in identification. The evaluation of different methods for each of the components of a face recognition system allowed the most suitable ones for a multi-spectral face recognition system in an uncontrolled environment to be selected. The experimental results, expressed in Rank-1 scores, were 99.5% and 99.6% in the TUFTS multi-spectral database with pose variation and expression variation, respectively, and 100.0% in the CASIA NIR-VIS 2.0 database, indicating that the use of multi-spectral images in an uncontrolled environment is advantageous when compared with the use of single spectral band images.<\/jats:p>","DOI":"10.3390\/s22114219","type":"journal-article","created":{"date-parts":[[2022,6,1]],"date-time":"2022-06-01T21:43:42Z","timestamp":1654119822000},"page":"4219","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Multispectral Facial Recognition in the Wild"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6752-3389","authenticated-orcid":false,"given":"Pedro","family":"Martins","sequence":"first","affiliation":[{"name":"Military Electrical and Computer Engineering, Portuguese Military Academy, Rua Gomes Freire, 1169-203 Lisbon, Portugal"},{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7529-6422","authenticated-orcid":false,"given":"Jos\u00e9 Silvestre","family":"Silva","sequence":"additional","affiliation":[{"name":"Military Electrical and Computer Engineering, Portuguese Military Academy, Rua Gomes Freire, 1169-203 Lisbon, Portugal"},{"name":"Military Academy Research Center (CINAMIL), Rua Gomes Freire, 1169-203 Lisbon, Portugal"},{"name":"Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UC), 3000-370 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3991-1269","authenticated-orcid":false,"given":"Alexandre","family":"Bernardino","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal"},{"name":"Institute for Systems and Robotics (ISR), 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,1]]},"reference":[{"key":"ref_1","first-page":"143","article-title":"Detection of Camouflaged People","volume":"5","author":"Bento","year":"2016","journal-title":"Int. J. Sens. Netw. Data Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Gon\u00e7alves, M., Silva, J.S., and Bioucas-Dias, J. (2015, January 21\u201322). Classification of Vegetation Types in Military Region. Proceedings of the SPIE Security and Defence 2015 Europe: Electro-Optical Remote Sensing, Photonic Technologies, and Applications, Toulouse, France.","DOI":"10.1117\/12.2194185"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"9341","DOI":"10.1109\/JSEN.2019.2925203","article-title":"Landmine Detection Using Multispectral Images","volume":"19","author":"Silva","year":"2019","journal-title":"IEEE Sens. J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Chambino, L.L., Silva, J.S., and Bernardino, A. (2021). Multispectral Face Recognition Using Transfer Learning with Adaptation of Domain Specific Units. Sensors, 21.","DOI":"10.3390\/s21134520"},{"key":"ref_5","unstructured":"Masi, I., Wu, Y., Hassner, T., and Natarajan, P. (November, January 29). Deep face recognition: A survey. Proceedings of the 31st SIBGRAPI Conference on Graphics, Patterns and Images, Foz do Igua\u00e7u, Paran\u00e1, Brazil."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"207871","DOI":"10.1109\/ACCESS.2020.3037451","article-title":"Multispectral Facial Recognition: A Review","volume":"8","author":"Chambino","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"102660","DOI":"10.1016\/j.jvcir.2019.102660","article-title":"An extensive review on spectral imaging in biometric systems: Challenges & advancements","volume":"65","author":"Munir","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2018.2803179","article-title":"Improving Shadow Suppression for Illumination Robust Face Recognition","volume":"41","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1109\/TPAMI.2018.2884458","article-title":"A Comprehensive Database for Benchmarking Imaging Systems","volume":"42","author":"Panetta","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_10","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Tran, L., Yin, X., and Liu, X. (2017, January 21\u201326). Disentangled representation learning gan for pose-invariant face recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2017.141"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1109\/TPAMI.2018.2858819","article-title":"3d-aided dual-agent gans for unconstrained face recognition","volume":"41","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1007\/s11263-019-01229-6","article-title":"Towards high fidelity face frontalization in the wild","volume":"128","author":"Cao","year":"2019","journal-title":"Int. J. Comput. Vis."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Qian, Y., Deng, W., and Hu, J. (2019, January 15\u201320). Unsupervised face normalization with extreme pose and expression in the wild. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.01008"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tuan Tran, A., Hassner, T., Masi, I., and Medioni, G. (2017, January 21\u201326). Regressing robust and discriminative 3D morphable models with a very deep neural network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Juan, PR, USA.","DOI":"10.1109\/CVPR.2017.163"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"17859","DOI":"10.1007\/s11042-020-08628-9","article-title":"Optimal fusion aided face recognition from visible and thermal face images","volume":"79","author":"Kanmani","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1109\/TPAMI.2016.2542816","article-title":"Graphical Representation for Heterogeneous Face Recognition","volume":"39","author":"Peng","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1109\/TPAMI.2019.2961900","article-title":"Adversarial cross-spectral face completion for NIR-VIS face recognition","volume":"42","author":"He","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"75305","DOI":"10.1109\/ACCESS.2019.2920855","article-title":"Heterogeneous Face Recognition Based on Multiple Deep Networks with Scatter Loss and Diversity Combination","volume":"7","author":"Hu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1109\/TPAMI.2018.2842770","article-title":"Wasserstein CNN: Learning Invariant Features for NIR-VIS Face Recognition","volume":"41","author":"He","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Li, S., Yi, D., Lei, Z., and Liao, S. (2013, January 23\u201328). The CASIA NIR-VIS 2.0 Face Database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Portland, OR, USA.","DOI":"10.1109\/CVPRW.2013.59"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/978-3-319-46448-0_2","article-title":"SSD: Single shot multibox detector","volume":"9905","author":"Liu","year":"2016","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, S., Zhu, X., Lei, Z., Shi, H., Wang, X., and Li, S.Z. (2017, January 22\u201329). S3FD: Single shot scale-invariant face detector. Proceedings of the IEEE International Conference on Computer Vision, Cambridge, MA, USA.","DOI":"10.1109\/ICCV.2017.30"},{"key":"ref_24","unstructured":"Bradski, G., and Kaehler, A. (2008). Learning OpenCV: Computer Vision with the OpenCV Library, O\u2019Reilly Media, Inc."},{"key":"ref_25","unstructured":"Li, J., Wang, Y., Wang, C., Tai, Y., Qian, J., Yang, J., Wang, C., Li, J., and Huang, F. (November, January 27). DSFD: Dual shot face detector. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_26","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 Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.241"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bulat, A., and Tzimiropoulos, G. (2017, January 22\u201329). How Far Are We from Solving the 2d & 3d Face Alignment Problem? (And a Dataset of 230,000 3d Facial Landmarks). Proceedings of the IEEE International Conference on Computer Vision, Cambridge, MA, USA.","DOI":"10.1109\/ICCV.2017.116"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Newell, A., Yang, K., and Deng, J. (2016, January 27\u201330). Stacked hourglass networks for human pose estimation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46484-8_29"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wei, Y., Liu, M., Wang, H., Zhu, R., Hu, G., and Zuo, W. (2020, January 13\u201319). Learning flow-based feature warping for face frontalization with illumination inconsistent supervision. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58610-2_33"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2884","DOI":"10.1109\/TIFS.2018.2833032","article-title":"A Light CNN for Deep Face Representation with Noisy Labels","volume":"13","author":"Wu","year":"2018","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_31","unstructured":"Minaee, S., Abdolrashidi, A., Su, H., Bennamoun, M., and Zhang, D. (2019). Biometrics recognition using deep learning: A survey. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.imavis.2009.08.002","article-title":"Multi-pie","volume":"28","author":"Gross","year":"2010","journal-title":"Image Vis. Comput."},{"key":"ref_33","unstructured":"(2021, March 27). Dataset 02: IRIS Thermal\/Visible Face Databases. Available online: http:\/\/vcipl-okstate.org\/pbvs\/bench\/."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Borade, S.N., Deshmukh, R.R., and Shrishrimal, P. (2016). Effect of distance measures on the performance of face recognition using principal component analysis. Intelligent Systems Technologies and Applications, Springer.","DOI":"10.1007\/978-3-319-23036-8_50"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1016\/j.patcog.2013.06.023","article-title":"Discriminant analysis and similarity measure","volume":"47","author":"Liu","year":"2014","journal-title":"Pattern Recognit."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4219\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:23:18Z","timestamp":1760138598000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4219"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,1]]},"references-count":35,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22114219"],"URL":"https:\/\/doi.org\/10.3390\/s22114219","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2022,6,1]]}}}