{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:16:06Z","timestamp":1760148966776,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"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>This paper presents a novel, autonomous learning system working in real-time for face recognition. Multiple convolutional neural networks for face recognition tasks are available; however, these networks need training data and a relatively long training process as the training speed depends on hardware characteristics. Pretrained convolutional neural networks could be useful for encoding face images (after classifier layers are removed). This system uses a pretrained ResNet50 model to encode face images from a camera and the Multinomial Na\u00efve Bayes for autonomous training in the real-time classification of persons. Faces of several persons visible in a camera are tracked using special cognitive tracking agents who deal with machine learning models. After a face in a new position of the frame appears (in a place where there was no face in the previous frames), the system checks if it is novel or not using a novelty detection algorithm based on an SVM classifier; if it is unknown, the system automatically starts training. As a result of the conducted experiments, one can conclude that good conditions provide assurance that the system can learn the faces of a new person who appears in the frame correctly. Based on our research, we can conclude that the critical element of this system working is the novelty detection algorithm. If false novelty detection works, the system can assign two or more different identities or classify a new person into one of the existing groups.<\/jats:p>","DOI":"10.3390\/s23125554","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T02:01:40Z","timestamp":1686708100000},"page":"5554","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Autonomous Face Classification Online Self-Training System Using Pretrained ResNet50 and Multinomial Na\u00efve Bayes"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8657-3472","authenticated-orcid":false,"given":"\u0141ukasz","family":"Maciura","sequence":"first","affiliation":[{"name":"Research and Development Center, Netrix S.A., 20-704 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2712-6098","authenticated-orcid":false,"given":"Tomasz","family":"Cieplak","sequence":"additional","affiliation":[{"name":"Research and Development Center, Netrix S.A., 20-704 Lublin, Poland"},{"name":"Department of Organization of Enterprise, Faculty of Management, Lublin University of Technology, 20-618 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5727-979X","authenticated-orcid":false,"given":"Damian","family":"Pliszczuk","sequence":"additional","affiliation":[{"name":"Research and Development Center, Netrix S.A., 20-704 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7604-8559","authenticated-orcid":false,"given":"Micha\u0142","family":"Maj","sequence":"additional","affiliation":[{"name":"Research and Development Center, Netrix S.A., 20-704 Lublin, Poland"},{"name":"Faculty of Computer Science, WSEI University, 20-209 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3524-9151","authenticated-orcid":false,"given":"Tomasz","family":"Rymarczyk","sequence":"additional","affiliation":[{"name":"Research and Development Center, Netrix S.A., 20-704 Lublin, Poland"},{"name":"Faculty of Computer Science, WSEI University, 20-209 Lublin, Poland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","unstructured":"Ahmad, S., and Hawkins, J. (2015). Properties of Sparse Distributed Representations and their Application to Hierarchical Temporal Memory. arXiv."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1109\/TNNLS.2019.2921143","article-title":"Associative Memories with Synaptic Delays","volume":"31","author":"Starzyk","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3736","DOI":"10.1109\/TNNLS.2020.3041048","article-title":"Concurrent Associative Memories with Synaptic Delays","volume":"32","author":"Starzyk","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_4","unstructured":"Huang, D., and Peng, Z. (2023, April 03). Unsupervised Face Recognition via Meta\u2014Learning. Available online: https:\/\/cs330.stanford.edu."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Boutros, F., Klemt, M., Fang, M., Kuijper, A., and Damer, N. (2022). Unsupervised Face Recognition using Unlabeled Synthetic Data. arXiv.","DOI":"10.1109\/FG57933.2023.10042627"},{"key":"ref_6","first-page":"238","article-title":"Face Recognition Using Unsupervised Feature Learning (UFL) Approach","volume":"24","author":"Therasa","year":"2016","journal-title":"Middle-East J. Sci. Res."},{"key":"ref_7","first-page":"518","article-title":"A Novelty Detection Approach to Classification","volume":"1","author":"Japkowicz","year":"1995","journal-title":"ICAI"},{"key":"ref_8","unstructured":"Kliger, M., and Fleishman, S. (2018). Novelty Detection with GAN. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5099","DOI":"10.1007\/s00500-023-07963-x","article-title":"Multi-similarity semi-supervised manifold embedding for facial attractiveness scoring","volume":"27","author":"Dornaika","year":"2023","journal-title":"Soft Comput."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"507","DOI":"10.18280\/ts.360605","article-title":"Gender Classification in Human Face Images for Smart Phone Applications Based on Local Texture Information and Evaluated Kuulback Leibler Divergence","volume":"36","year":"2019","journal-title":"Traitement Du Signal"},{"key":"ref_11","first-page":"4068","article-title":"Realtime face matching and gender prediction based on deep learning","volume":"13","author":"Surinwarangkoon","year":"2023","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1250","DOI":"10.1016\/j.patrec.2011.03.019","article-title":"Combined pattern search optimization of feature extraction and classification parameters in facial recognition","volume":"32","author":"Caleanu","year":"2011","journal-title":"Patter Recognit. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.neunet.2020.12.003","article-title":"A comprehensive study of class incremental learning algorithms for visual tasks","volume":"135","author":"Belouadah","year":"2020","journal-title":"Neural Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Li, L., Jun, Z., Fei, J., and Li, S. (2017, January 8\u201312). An Incremental Face Recognition System Based on Deep Learning. Proceedings of the 2017 Fifteenth IAPR International Conference on Machine Vision Applications (MVA), Nagoya, Japan.","DOI":"10.23919\/MVA.2017.7986845"},{"key":"ref_15","first-page":"4945","article-title":"River: Machine learning for streaming data in Python","volume":"22","author":"Montiel","year":"2021","journal-title":"J. Mach. Learn. Res."},{"key":"ref_16","unstructured":"(2023, May 25). VowpalWabbit Machine Learning System. Available online: https:\/\/github.com\/VowpalWabbit\/vowpal_wabbit."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_18","first-page":"1","article-title":"Comparative Analysis of Na\u00efve Bayesian Techniques in Health\u2014Related for Classification Task","volume":"1","author":"Ismail","year":"2020","journal-title":"J. Soft Comput. Data Min."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Singh, G., Kumar, B., Gaur, L., and Tyagi, A. (2019, January 24\u201326). Comparison between Multinomial and Bernoulli Na\u00efve Bayes for Text Classification. Proceedings of the International Conference on Automation Computational and Technology Management (ICACTM), London, UK.","DOI":"10.1109\/ICACTM.2019.8776800"},{"key":"ref_20","first-page":"75","article-title":"Performance of Na\u00efve and Complement Na\u00efve Bayes Algorithms Based on Accuracy, Precision and Recall Performance Evaluation Criterions","volume":"8","author":"Seref","year":"2019","journal-title":"Int. J. Comput. Acad. Res."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Bi, Z.-J., Han, Y.-Q., Huang, C.-Q., and Wang, M. (2019, January 26\u201327). Gaussian Naive Bayesian Data Classification Model Based on Clustering Algorithm. Proceedings of the 2019 International Conference on Modeling, Analysis, Simulation Technologies and Applications (MASTA 2019), Hangzhou, China.","DOI":"10.2991\/masta-19.2019.67"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support\u2014Vector Networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_23","unstructured":"Evgeniou, T., and Pontil, M. (2001). Machine Learning and Its Applications, Advanced Lectures, Springer."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1023\/B:VISI.0000013087.49260.fb","article-title":"Jones, Robust real\u2014Time face detection","volume":"57","author":"Viola","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_25","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 recognizing face across pose and age. Proceedings of the International Conference on Automatic Face and Gesture Recognition, Xi\u2019an, China.","DOI":"10.1109\/FG.2018.00020"},{"key":"ref_26","unstructured":"(2022, October 26). Available online: https:\/\/github.com\/WeidiXie\/Keras-VGGFace2-ResNet50."},{"key":"ref_27","unstructured":"(2023, February 01). Available online: https:\/\/www.kaggle.com\/datasets\/hereisburak\/pins-face-recognition."},{"key":"ref_28","first-page":"155","article-title":"Optimizing Stochastic Gradient Descent in Text Classification Based on Fine-Tuning Hyper-Parameters Approach","volume":"16","author":"Diab","year":"2018","journal-title":"Int. J. Comput. Sci. Inf. Secur."},{"key":"ref_29","unstructured":"Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and Regression Trees, Wadsworth & Brooks."},{"key":"ref_30","first-page":"3","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Charine Learn."},{"key":"ref_31","unstructured":"Kartynnik, Y., Ablavatski, A., and Grishchenko, I. (2019). Grundmann, Real\u2014Time Facial Surface Geometry from Monocular Video on Mobile GPUs. arXiv."},{"key":"ref_32","unstructured":"(2023, February 16). Available online: https:\/\/www.youtube.com\/watch?v=ZWZX-tDgcQE."},{"key":"ref_33","unstructured":"(2023, May 04). Available online: https:\/\/www.youtube.com\/watch?v=AYKGGVfCCZ0."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5554\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:54:24Z","timestamp":1760126064000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/12\/5554"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,14]]},"references-count":33,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,6]]}},"alternative-id":["s23125554"],"URL":"https:\/\/doi.org\/10.3390\/s23125554","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2023,6,14]]}}}