Autenticación de personas utilizando un clasificador SVM
Palabras clave:
Extracción de características, SVM, Autenticación de personas
Resumen
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.
Referencias bibliográficas
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Zhang, B. (2019). Distributed SVM face recognition based on Hadoop. Cluster Computing, 22(S1). https://doi.org/10.1007/s10586-017-1330-5
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
Cómo citar
Aparicio-Arroyo, A. A., Olmos-Pineda, I., & Olvera-López , J. A. (2021). Autenticación de personas utilizando un clasificador SVM. Revista Colombiana De Computación, 22(2), 48–57. Recuperado a partir de https://revistas.unab.edu.co/index.php/rcc/article/view/4299
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Publicado
2021-12-01
Sección
Artículo de investigación científica y tecnológica
