Autenticación de personas utilizando un clasificador SVM

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dc.contributor.authorAparicio Arroyo, Aida A.
dc.contributor.authorOlmos Pineda, Ivan
dc.contributor.authorOlvera López, J. Arturo
dc.contributor.orcidAparicio Arroyo, Aida A. [0000-0002-3547-2433]spa
dc.contributor.orcidOlmos Pineda, Ivan [0000-0003-1698-000X]spa
dc.contributor.orcidOlvera López, J. Arturo [0000-0003-0639-1463]spa
dc.date.accessioned2024-09-12T19:51:36Z
dc.date.available2024-09-12T19:51:36Z
dc.date.issued2021-09-16
dc.description.abstractEn los últimos años, la autenticación de personas ha tomado un gran auge debido a los avances tecnológicos e investigaciones que se han desarrollado alrededor del tema. En este proceso se usan técnicas de visión por computadora que permiten procesar una imagen o video para determinar la identidad de una persona. En el presente artículo, se analizan trabajos relacionados con el proceso de autenticación de personas, haciendo un análisis profundo en los trabajos basados en Máquina de Vectores de Soporte (Support Vector Machines). De igual manera, se explican a grandes rasgos las diferentes etapas que conforman el proceso de autenticación de personas. Finalmente, se presenta un conjunto de experimentos realizados, utilizando una combinación de características basadas en color, textura y simetría, mientras que, para la etapa de clasificación se utiliza SVM. Esta combinación de características aunada con el clasificador, muestra ser una alternativa para la autenticación de personas.spa
dc.description.abstractenglishIn recent years, people’s authentication has taken a significant boom due to technological advances and research developed around the subject. In this process, computer vision techniques are used to process an image or video to determine a person’s identity. In this article, we analyzed related works to the people authentication process, making a deep analysis in the works based on Support Vector Machines (SVM). In the same way, we roughly explained the stages that make up the process of people authentication. Finally, we present a set of experiments performed, using a feature combination based on color, texture, and symmetry. In contrast, SVM is used for the classification stage. This combination of features, together with the classifier, shows to be an alternative to people authentication.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.29375/25392115.4299
dc.identifier.instnameinstname:Universidad Autónoma de Bucaramanga UNABspa
dc.identifier.issnISSN: 1657-2831spa
dc.identifier.issne-ISSN: 2539-2115spa
dc.identifier.repourlrepourl:https://repository.unab.edu.cospa
dc.identifier.urihttp://hdl.handle.net/20.500.12749/26486
dc.language.isospaspa
dc.publisherUniversidad Autónoma de Bucaramanga UNABspa
dc.relationhttps://revistas.unab.edu.co/index.php/rcc/article/view/4299/3507spa
dc.relation.referencesAdjabi, I., Ouahabi, A., Benzaoui, A., & Taleb-Ahmed, A. (2020). Past, Present, and Future of Face Recognition: A Review. Electronics, 9(8). https://doi.org/10.3390/electronics9081188
dc.relation.referencesAlmabdy, S., & Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Applied Sciences, 9(20). https://doi.org/10.3390/app9204397
dc.relation.referencesBalamurugan, A., & Suganya, B. (2021). An Efficient Real Time Face Expression Identification System Using SVM. Journal of Physics: Conference Series, 1916(1). https://doi.org/10.1088/1742-6596/1916/1/012229
dc.relation.referencesBindu, H., & Manjunathachary, K. (2019). Kernel-based scale-invariant feature transform and spherical SVM classifier for face recognition. Journal of Engineering Research, 7(3), 142–160. https://kuwaitjournals.org/jer/index.php/JER/article/view/4177
dc.relation.referencesChen, H., & Haoyu, C. (2019). Face Recognition Algorithm Based on VGG Network Model and SVM. Journal of Physics: Conference Series, 1229. https://doi.org/10.1088/1742-6596/1229/1/012015
dc.relation.referencesDe-la-Torre, M., Granger, E., Radtke, P. V. W., Sabourin, R., & Gorodnichy, D. O. (2015). Partially-supervised learning from facial trajectories for face recognition in video surveillance. Information Fusion, 24. https://doi.org/10.1016/j.inffus.2014.05.006
dc.relation.referencesDino, H. I., & Abdulrazzaq, M. B. (2019, April). Facial Expression Classification Based on SVM, KNN and MLP Classifiers. 2019 International Conference on Advanced Science and Engineering (ICOASE). https://doi.org/10.1109/ICOASE.2019.8723728
dc.relation.referencesGeorge, M., Sivan, A., Jose, B. R., & Mathew, J. (2019). Real-time single-view face detection and face recognition based on aggregate channel feature. International Journal of Biometrics, 11(3). https://doi.org/10.1504/IJBM.2019.100829
dc.relation.referencesGhazal, M. T., & Abdullah, K. (2020). Face recognition based on curvelets, invariant moments features and SVM. TELKOMNIKA (Telecommunication Computing Electronics and Control), 18(2), 733–739. https://doi.org/10.12928/telkomnika.v18i2.14106
dc.relation.referencesHu, L., & Cui, J. (2019). Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm. Measurement, 145. https://doi.org/10.1016/j.measurement.2019.02.006
dc.relation.referencesHuang, H., & Zhu, J. (2021). A Short Review of the Application of Machine Learning Methods in Smart Airports. Journal of Physics: Conference Series, 1769. https://doi.org/10.1088/1742-6596/1769/1/012010
dc.relation.referencesJain, A. K., Ross, A. A., & Nandakumar, K. (2011). Introduction to Biometrics. Springer US. https://doi.org/10.1007/978-0-387-77326-1
dc.relation.referencesKar, N. B., Babu, K. S., Sangaiah, A. K., & Bakshi, S. (2019). Face expression recognition system based on ripplet transform type II and least square SVM. Multimedia Tools and Applications, 78(4). https://doi.org/10.1007/s11042-017-5485-0
dc.relation.referencesLiu, X., Cheng, X., & Lee, K. (2020). GA-SVM based Facial Emotion Recognition using Facial Geometric Features. IEEE Sensors Journal, 21(10), 11532–11542. https://doi.org/10.1109/JSEN.2020.3028075
dc.relation.referencesReddy, C. V. R., Reddy, U. S., & Kishore, K. V. K. (2019). Facial Emotion Recognition Using NLPCA and SVM. Traitement Du Signal, 36(1). https://doi.org/10.18280/ts.360102
dc.relation.referencesSagonas, C., Tzimiropoulos, G., Zafeiriou, S., & Pantic, M. (2013). A Semi-automatic Methodology for Facial Landmark Annotation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops.
dc.relation.referencesShah, J. H., Sharif, M., Yasmin, M., & Fernandes, S. L. (2020). Facial expressions classification and false label reduction using LDA and threefold SVM. Pattern Recognition Letters, 139. https://doi.org/10.1016/j.patrec.2017.06.021
dc.relation.referencesSingh, S., Singh, D., & Yadav, V. (2020). Face Recognition Using HOG Feature Extraction and SVM Classifier. International Journal of Emerging Trends in Engineering Research, 8(9). https://doi.org/10.30534/ijeter/2020/244892020
dc.relation.referencesVengatesan, K., Kumar, A., Karuppuchamy, V., Shaktivel, R., & Singhal, A. (2019, December). Face Recognition of Identical Twins Based On Support Vector Machine Classifier. 2019 Third International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). https://doi.org/10.1109/I-SMAC47947.2019.9032548
dc.relation.referencesZhang, B. (2019). Distributed SVM face recognition based on Hadoop. Cluster Computing, 22(S1). https://doi.org/10.1007/s10586-017-1330-5
dc.relation.urihttps://revistas.unab.edu.co/index.php/rcc/issue/view/276spa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.sourceVol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 48-57spa
dc.subjectExtracción de característicasspa
dc.subjectSVMspa
dc.subjectAutenticación de personasspa
dc.subject.keywordsFeature extractioneng
dc.subject.keywordsSVMeng
dc.subject.keywordsPeople authenticationeng
dc.titleAutenticación de personas utilizando un clasificador SVMspa
dc.title.translatedPeople authentication through a SVM classifiereng
dc.type.coarhttp://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.type.driverinfo:eu-repo/semantics/article
dc.type.hasversioninfo:eu-repo/semantics/publishedVersion
dc.type.localArtículospa
dc.type.redcolhttp://purl.org/redcol/resource_type/ART

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