{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T02:53:36Z","timestamp":1774493616675,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,10,9]],"date-time":"2022-10-09T00:00:00Z","timestamp":1665273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Economy and Competitiveness (MINECO) of the Spanish Government","award":["PID2020-116417RB-C44"],"award-info":[{"award-number":["PID2020-116417RB-C44"]}]},{"name":"Ministry of Economy and Competitiveness (MINECO) of the Spanish Government","award":["GA N\u00ba 857159"],"award-info":[{"award-number":["GA N\u00ba 857159"]}]},{"name":"EU\u2019s Horizon 2020 programme","award":["PID2020-116417RB-C44"],"award-info":[{"award-number":["PID2020-116417RB-C44"]}]},{"name":"EU\u2019s Horizon 2020 programme","award":["GA N\u00ba 857159"],"award-info":[{"award-number":["GA N\u00ba 857159"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects\/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.<\/jats:p>","DOI":"10.3390\/s22197641","type":"journal-article","created":{"date-parts":[[2022,10,10]],"date-time":"2022-10-10T05:12:21Z","timestamp":1665378741000},"page":"7641","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7641-4643","authenticated-orcid":false,"given":"Juli\u00e1n","family":"Caba","sequence":"first","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1931-3245","authenticated-orcid":false,"given":"Jes\u00fas","family":"Barba","sequence":"additional","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4688-8650","authenticated-orcid":false,"given":"Fernando","family":"Rinc\u00f3n","sequence":"additional","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3188-4633","authenticated-orcid":false,"given":"Jos\u00e9 Antonio","family":"de la Torre","sequence":"additional","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8019-9640","authenticated-orcid":false,"given":"Soledad","family":"Escolar","sequence":"additional","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7372-1568","authenticated-orcid":false,"given":"Juan Carlos","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Technology and Information Systems Department, School of Computer Science, University of Castilla-La Mancha, 13071 Ciudad Real, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1789","DOI":"10.1109\/TIFS.2012.2214212","article-title":"Face Recognition Performance: Role of Demographic Information","volume":"7","author":"Klare","year":"2012","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ni, H., Ren, F., and Kang, X. (2019, January 4\u20137). Face and Gender Recognition System Based on Convolutional Neural networks. Proceedings of the 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China.","DOI":"10.1109\/ICMA.2019.8816192"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1109\/TAFFC.2015.2485205","article-title":"A Main Directional Mean Optical Flow Feature for Spontaneous Micro-Expression Recognition","volume":"7","author":"Liu","year":"2016","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.envpol.2015.05.041","article-title":"Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images","volume":"205","author":"Arellano","year":"2015","journal-title":"Environ. Pollut."},{"key":"ref_5","first-page":"1","article-title":"Application of airborne and spaceborne hyperspectral imaging techniques for atmospheric research: Past, present, and future","volume":"56","author":"Calin","year":"2020","journal-title":"Appl. Spectrosc. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Qureshi, R., Uzair, M., and Zahra, A. (2020). Current Advances in Hyperspectral Face Recognition. arXiv.","DOI":"10.36227\/techrxiv.12136425.v1"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alzu\u2019bi, A., Albalas, F., AL-Hadhrami, T., Younis, L.B., and Bashayreh, A. (2021). Masked Face Recognition Using Deep Learning: A Review. Electronics, 10.","DOI":"10.3390\/electronics10212666"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jignesh Chowdary, G., Punn, N.S., Sonbhadra, S.K., and Agarwal, S. (2020). Face Mask Detection Using Transfer Learning of InceptionV3. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-030-66665-1_6"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108288","DOI":"10.1016\/j.measurement.2020.108288","article-title":"A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic","volume":"167","author":"Loey","year":"2021","journal-title":"Measurement"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 24\u201330). 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_11","doi-asserted-by":"crossref","first-page":"19753","DOI":"10.1007\/s11042-021-10711-8","article-title":"Face mask detection using YOLOv3 and faster R-CNN models: COVID-19 environment","volume":"80","author":"Singh","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Vinh, T.Q., and Anh, N.T.N. (2020, January 25\u201327). Real-Time Face Mask Detector Using YOLOv3 Algorithm and Haar Cascade Classifier. Proceedings of the 2020 International Conference on Advanced Computing and Applications (ACOMP), Quy Nhon, Vietnam.","DOI":"10.1109\/ACOMP50827.2020.00029"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104341","DOI":"10.1016\/j.imavis.2021.104341","article-title":"FMD-Yolo: An efficient face mask detection method for COVID-19 prevention and control in public","volume":"117","author":"Wu","year":"2022","journal-title":"Image Vis. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4475","DOI":"10.1007\/s11042-021-11772-5","article-title":"Face mask detection and classification via deep transfer learning","volume":"81","author":"Su","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"44276","DOI":"10.1109\/ACCESS.2020.2977386","article-title":"A Novel GAN-Based Network for Unmasking of Masked Face","volume":"8","author":"Javed","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","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_17","first-page":"4284","article-title":"3D Face Recognition Using Geodesic Facial Curves to Handle Expression, Occlusion and Pose Variations","volume":"5","author":"Gawali","year":"2014","journal-title":"Int. J. Comput. Sci. IT"},{"key":"ref_18","first-page":"605","article-title":"Efficient masked face recognition method during the COVID-19 pandemic","volume":"5","author":"Hariri","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Boutros, F., Damer, N., Kirchbuchner, F., and Kuijper, A. (2021). Unmasking Face Embeddings by Self-restrained Triplet Loss for Accurate Masked Face Recognition. arXiv.","DOI":"10.1016\/j.patcog.2021.108473"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, Y., Gao, X., and Han, Z. (2018). MobileFaceNets: Efficient CNNs for Accurate Real-time Face Verification on Mobile Devices, Springer.","DOI":"10.1007\/978-3-319-97909-0_46"},{"key":"ref_21","unstructured":"Anwar, A., and Raychowdhury, A. (2020). Masked Face Recognition for Secure Authentication. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Cao, Q., Shen, L., Xie, W., Parkhi, O.M., and Zisserman, A. (2018, January 15\u201319). VGGFace2: A Dataset for Recognising Faces across Pose and Age. Proceedings of the 2018 13th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2018), Los Alamitos, CA, USA.","DOI":"10.1109\/FG.2018.00020"},{"key":"ref_23","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_24","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_25","doi-asserted-by":"crossref","unstructured":"Bhattacharya, S., Das, S., and Routray, A. (2018, January 3\u20137). Graph Manifold Clustering based Band Selection for Hyperspectral Face Recognition. Proceedings of the 2018 26th European Signal Processing Conference (EUSIPCO), Rome, Italy.","DOI":"10.23919\/EUSIPCO.2018.8553006"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"24333","DOI":"10.1109\/ACCESS.2019.2897213","article-title":"Hierarchical Clustering Based Band Selection Algorithm for Hyperspectral Face Recognition","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_27","unstructured":"Sharma, V., Diba, A., Tuytelaars, T., and Gool, L.V. (2016). Hyperspectral CNN for Image Classification & Band Selection, with Application to Face Recognition, KU Leuven. Technical Report."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1552","DOI":"10.1109\/TPAMI.2003.1251148","article-title":"Face recognition in hyperspectral images","volume":"25","author":"Pan","year":"2003","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1354","DOI":"10.1109\/TSMCA.2010.2052603","article-title":"Studies on Hyperspectral Face Recognition in Visible Spectrum with Feature Band Selection","volume":"40","author":"Di","year":"2010","journal-title":"IEEE Trans. Syst. Man Cybern.-Part A Syst. Hum."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Becattini, F., Song, X., Baecchi, C., Fang, S.T., Ferrari, C., Nie, L., and Del Bimbo, A. (2021). PLM-IPE: A Pixel-Landmark Mutual Enhanced Framework for Implicit Preference Estimation. ACM Multimedia Asia, Association for Computing Machinery.","DOI":"10.1145\/3469877.3490621"},{"key":"ref_31","first-page":"1","article-title":"Parallel Support Vector Machines: The Cascade SVM","volume":"Volume 17","author":"Saul","year":"2005","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1049\/iet-ipr.2016.0722","article-title":"Hyperspectral face recognition via feature extraction and CRC-based classifier","volume":"11","author":"Chen","year":"2017","journal-title":"IET Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"164148","DOI":"10.1109\/ACCESS.2021.3133446","article-title":"Integrated Single Shot Multi-Box Detector and Efficient Pre-Trained Deep Convolutional Neural Network for Partially Occluded Face Recognition System","volume":"9","author":"Tsai","year":"2021","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Almabdy, S., and Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Appl. Sci., 9.","DOI":"10.3390\/app9204397"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/S1005-8885(13)60038-2","article-title":"Facial expression feature extraction using hybrid PCA and LBP","volume":"20","author":"Yuan","year":"2013","journal-title":"J. China Univ. Posts Telecommun."},{"key":"ref_36","first-page":"708","article-title":"An Occluded Facial Expression Recognition Method Based on Sparse Representation","volume":"27","author":"Zhu","year":"2014","journal-title":"Public Relat. Artif. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yeh, R.A., Chen, C., Lim, T.Y., Hasegawa-Johnson, M., and Do, M.N. (2016). Semantic Image Inpainting with Perceptual and Contextual Losses. arXiv.","DOI":"10.1109\/CVPR.2017.728"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7641\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:48:29Z","timestamp":1760143709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7641"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,9]]},"references-count":37,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197641"],"URL":"https:\/\/doi.org\/10.3390\/s22197641","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,9]]}}}