Autenticación de personas utilizando un clasificador SVM

En 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...

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Autores:: Aparicio Arroyo, Aida A.
Olmos Pineda, Ivan
Olvera López, J. Arturo

Tipo de recurso:: Article of investigation

Fecha de publicación:: 2021

Institución:: Universidad Autónoma de Bucaramanga - UNAB

Repositorio:: Repositorio UNAB

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Autenticación de personas utilizando un clasificador SVM
dc.title.translated.eng.fl_str_mv	People authentication through a SVM classifier
title	Autenticación de personas utilizando un clasificador SVM
spellingShingle	Autenticación de personas utilizando un clasificador SVM Extracción de características SVM Autenticación de personas Feature extraction SVM People authentication
title_short	Autenticación de personas utilizando un clasificador SVM
title_full	Autenticación de personas utilizando un clasificador SVM
title_fullStr	Autenticación de personas utilizando un clasificador SVM
title_full_unstemmed	Autenticación de personas utilizando un clasificador SVM
title_sort	Autenticación de personas utilizando un clasificador SVM
dc.creator.fl_str_mv	Aparicio Arroyo, Aida A. Olmos Pineda, Ivan Olvera López, J. Arturo
dc.contributor.author.none.fl_str_mv	Aparicio Arroyo, Aida A. Olmos Pineda, Ivan Olvera López, J. Arturo
dc.contributor.orcid.spa.fl_str_mv	Aparicio Arroyo, Aida A. [0000-0002-3547-2433] Olmos Pineda, Ivan [0000-0003-1698-000X] Olvera López, J. Arturo [0000-0003-0639-1463]
dc.subject.spa.fl_str_mv	Extracción de características SVM Autenticación de personas
topic	Extracción de características SVM Autenticación de personas Feature extraction SVM People authentication
dc.subject.keywords.eng.fl_str_mv	Feature extraction SVM People authentication
description	En 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.
publishDate	2021
dc.date.issued.none.fl_str_mv	2021-09-16
dc.date.accessioned.none.fl_str_mv	2024-09-12T19:51:36Z
dc.date.available.none.fl_str_mv	2024-09-12T19:51:36Z
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dc.relation.references.none.fl_str_mv	Adjabi, 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 Almabdy, S., & Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Applied Sciences, 9(20). https://doi.org/10.3390/app9204397 Balamurugan, 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 Bindu, 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 Chen, 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 De-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 Dino, 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 George, 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 Ghazal, 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 Hu, 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 Huang, 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 Jain, A. K., Ross, A. A., & Nandakumar, K. (2011). Introduction to Biometrics. Springer US. https://doi.org/10.1007/978-0-387-77326-1 Kar, 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 Liu, 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 Reddy, 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 Sagonas, 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. Shah, 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 Singh, 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 Vengatesan, 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 Zhang, B. (2019). Distributed SVM face recognition based on Hadoop. Cluster Computing, 22(S1). https://doi.org/10.1007/s10586-017-1330-5
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dc.source.spa.fl_str_mv	Vol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 48-57
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spelling	Aparicio Arroyo, Aida A.79f1aa4a-9b7a-4f6b-8696-90a275018366Olmos Pineda, Ivan1e28abd8-81ec-4aaa-8a38-528ade612bc9Olvera López, J. Arturoc2ee9a83-ad8a-454b-afa7-f539a545c84cAparicio Arroyo, Aida A. [0000-0002-3547-2433]Olmos Pineda, Ivan [0000-0003-1698-000X]Olvera López, J. Arturo [0000-0003-0639-1463]2024-09-12T19:51:36Z2024-09-12T19:51:36Z2021-09-16ISSN: 1657-2831e-ISSN: 2539-2115http://hdl.handle.net/20.500.12749/26486instname:Universidad Autónoma de Bucaramanga UNABrepourl:https://repository.unab.edu.cohttps://doi.org/10.29375/25392115.4299En 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.In 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.application/pdfspaUniversidad Autónoma de Bucaramanga UNABhttps://revistas.unab.edu.co/index.php/rcc/article/view/4299/3507https://revistas.unab.edu.co/index.php/rcc/issue/view/276Adjabi, 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/electronics9081188Almabdy, S., & Elrefaei, L. (2019). Deep Convolutional Neural Network-Based Approaches for Face Recognition. Applied Sciences, 9(20). https://doi.org/10.3390/app9204397Balamurugan, A., & Suganya, B. (2021). An Efficient Real Time Face Expression Identification System Using SVM. Journal of Physics: Conference Series, 1916(1). https://doi.org/10.1088/1742-6596/1916/1/012229Bindu, 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/4177Chen, H., & Haoyu, C. (2019). Face Recognition Algorithm Based on VGG Network Model and SVM. Journal of Physics: Conference Series, 1229. https://doi.org/10.1088/1742-6596/1229/1/012015De-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.2014.05.006Dino, 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.2019.8723728George, 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.2019.100829Ghazal, 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.v18i2.14106Hu, L., & Cui, J. (2019). Digital image recognition based on Fractional-order-PCA-SVM coupling algorithm. Measurement, 145. https://doi.org/10.1016/j.measurement.2019.02.006Huang, 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/10.1088/1742-6596/1769/1/012010Jain, A. K., Ross, A. A., & Nandakumar, K. (2011). Introduction to Biometrics. Springer US. https://doi.org/10.1007/978-0-387-77326-1Kar, 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/s11042-017-5485-0Liu, 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.2020.3028075Reddy, 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.360102Sagonas, 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.Shah, 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.2017.06.021Singh, 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/2020/244892020Vengatesan, 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-SMAC47947.2019.9032548Zhang, B. (2019). Distributed SVM face recognition based on Hadoop. Cluster Computing, 22(S1). https://doi.org/10.1007/s10586-017-1330-5Vol. 22 Núm. 2 (2021): Revista Colombiana de Computación (Julio-Diciembre); 48-57Extracción de característicasSVMAutenticación de personasFeature extractionSVMPeople authenticationAutenticación de personas utilizando un clasificador SVMPeople authentication through a SVM classifierinfo:eu-repo/semantics/articleArtículohttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/redcol/resource_type/ARThttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/access_right/c_abf2ORIGINALArtículo.pdfArtículo.pdfArtículoapplication/pdf1818055https://repository.unab.edu.co/bitstream/20.500.12749/26486/1/Art%c3%adculo.pdf3b22208f62686bf83442a1ed5acf5c42MD51open accessTHUMBNAILArtículo.pdf.jpgArtículo.pdf.jpgIM Thumbnailimage/jpeg9934https://repository.unab.edu.co/bitstream/20.500.12749/26486/3/Art%c3%adculo.pdf.jpgaa006ab475019014b9bfeb4780dc79d6MD53open accessLICENSElicense.txtlicense.txttext/plain; charset=utf-8347https://repository.unab.edu.co/bitstream/20.500.12749/26486/2/license.txt855f7d18ea80f5df821f7004dff2f316MD52open access20.500.12749/26486oai:repository.unab.edu.co:20.500.12749/264862024-09-12 22:02:30.134open accessRepositorio Institucional \| Universidad Autónoma de Bucaramanga - UNABrepositorio@unab.edu.coTGEgUmV2aXN0YSBDb2xvbWJpYW5hIGRlIENvbXB1dGFjacOzbiBlcyBmaW5hbmNpYWRhIHBvciBsYSBVbml2ZXJzaWRhZCBBdXTDs25vbWEgZGUgQnVjYXJhbWFuZ2EuIEVzdGEgUmV2aXN0YSBubyBjb2JyYSB0YXNhIGRlIHN1bWlzacOzbiB5IHB1YmxpY2FjacOzbiBkZSBhcnTDrWN1bG9zLiBQcm92ZWUgYWNjZXNvIGxpYnJlIGlubWVkaWF0byBhIHN1IGNvbnRlbmlkbyBiYWpvIGVsIHByaW5jaXBpbyBkZSBxdWUgaGFjZXIgZGlzcG9uaWJsZSBncmF0dWl0YW1lbnRlIGludmVzdGlnYWNpw7NuIGFsIHDDumJsaWNvIGFwb3lhIGEgdW4gbWF5b3IgaW50ZXJjYW1iaW8gZGUgY29ub2NpbWllbnRvIGdsb2JhbC4=

Autenticación de personas utilizando un clasificador SVM

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