Facial recognition techniques using SVM: A comparative analysis

Authors

DOI:

https://doi.org/10.29019/enfoque.v10n3.493

Keywords:

Databases, Support vector machine, Facial recognition, Artificial neural networks.

Abstract

This paper presents a literary review of facial recognition in 2D, which plays an important role in the life of the human being in terms of safety, work activity, etc. The focus is on the results obtained by some researchers with the application of feature extraction techniques, pattern classifiers, databases and their respective percentage of efficiency obtained. The objective is to determine efficient techniques that allow an optimal 2D facial recognition process, based on the quality of databases, feature extractors and pattern classifiers.

Downloads

Download data is not yet available.

References

Belén, A., Díaz, M., y Físicas, C. C. (2004). Reconocimiento Facial Automático mediante Técnicas de Visión Tridimensional Tesis Doctoral Reconocimiento Facial Automático mediante Técnicas de Visión Tridimensional Tesis Doctoral.
Benavides, D. J., Jurado, F., y González, L. G. (2018). Data analysis and tools applied to modeling and simulation of a PV system in Ecuador ( Análisis de datos y herramientas aplicadas al modelado y simulación de un sistema fotovoltaico en Ecuador ), 1-12.
Benavente, R., Martínez, A. (2014). The AR Face Database. Computer Vision Center, (January 1998): 1-8.
Benitez-Garcia, G., Olivares-Mercado, J., Aguilar-Torres, G., Sanchez-Perez, G., y Perez-Meana, H. (2012). Face Identification Based on Contrast Limited Adaptive Histogram Equalization (CLAHE), (mayo 2015).
Cabello Pardos, E. (2003). Técnicas de reconocimiento facial mediante redes neuronales, 135. Retrieved from http://dialnet.unirioja.es/servlet/tesis?codigo=2586&info=resumen&idioma=SPA
Calle-López, D., Cornejo-Reyes, J., Pesántez-Avilés, F., y Rodas, M. (2018). Un sistema experto basado en minería de datos y programación entera lineal para soporte en la asignación de materias y diseño de horarios en educación superior ( An expert system based on data mining and linear integer programming to support the timetabling design and courses assignment in higher education ), 102-117.
Chang, C., & Lin, C. (2013). LIBSVM : A Library for Support Vector Machines. ACM Transactions on Intelligent Systems and Technology (TIST), 2: 1-39. https://doi.org/10.1145/1961189.1961199
Correa, A. C., Salazar, A. E. S., y Ortiz, F. A. P. (2013). Reconocimiento de rostros y gestos faciales mediante un analisis de relecancia con imagenes 3D. Revista De Investigación, Desarrollo E Innovación, 4 (1): 7-20. https://doi.org/10.19053/20278306.2563
Deng, C. (2008). Four face databases in matlab format. Retrieved from http://www.cad.zju.edu.cn/home/dengcai/Data/FaceData.html
Devi, H. S., Laishram, R., y Thounaojam, D. M. (2015). Face Recognition using R-KDA with Non-Linear SVM for Multi-View Database. Procedia Computer Science, 54, 532-541. https://doi.org/10.1016/j.procs.2015.06.061
Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhan, X. (2008). The CAS-PEAL large-scale Chinese face database and baseline evaluations. IEE, 38 (1): 149-161.
Georghiades, A. (1997). Yale Face Database. Center for Computational Vision and Control, Yale.
González, M. (2007). Reconocimiento facial combinando técnicas 2D y 3D. Escola d'Enginyeria de Telecomunicació i Aeroespacial de Castelldefels. Barcelona.
Graham, D. B., y Allinson, N. (1998). Characterizing Virtual Eigensignatures for General Purpose Face Recognition. Face recognition: from theory to applications; 163 ; 446-456. Springer.
Gumus, E., Kilic, N., Sertbas, A., y Ucan, O. N. (2010). Evaluation of face recognition techniques using PCA, wavelets and SVM. Expert Systems with Applications, 37 (9): 6404–6408. https://doi.org/10.1016/j.eswa.2010.02.079
Guo, G., Li, S. Z., y Chan, K. L. (2001). Support vector machines for face recognition. Image and Vision Computing, 19, 631-638. https://doi.org/10.1016/S0262-8856(01)00046-4
Jos, C., Alamo, D., Arnaldo, L., Calla, R., y P, L. J. F. (2015). Efficient approach for interest points detection in non-rigid shapes.
Kasar, M. M., Bhattacharyya, D., y Kim, T. (2016). Face Recognition Using Neural Network : A Review, 10 (3): 81-100.
Khan, N. M., Ksantini, R., Ahmad, I. S., & Boufama, B. (2012). A novel SVM+NDA model for classification with an application to face recognition. Pattern Recognition, 45 (1): 66-79. https://doi.org/10.1016/j.patcog.2011.05.004
Kim, S.-K., Park, Y. J., Toh, K.-A., y Lee, S. (2010). SVM-based feature extraction for face recognition. Pattern Recognition, 43 (8): 2871-2881. https://doi.org/10.1016/j.patcog.2010.03.008
Kong, R., y Zhang, B. (2011). A New Face Recognition Method Based on Fast Least Squares Support Vector Machine. Physics Procedia, 22: 616-621. https://doi.org/10.1016/j.phpro.2011.11.095
Li, W., Liu, L., y Gong, W. (2011). Multi-objective uniform design as a SVM model selection tool for face recognition. Expert Systems with Applications, 38 (6): 6689-6695. https://doi.org/10.1016/j.eswa.2010.11.066
Lin, W.-H., Wang, P., y Tsai, C.-F. (2016). Face recognition using support vector model classifier for user authentication. Electronic Commerce Research and Applications, 18 (February), 1-12. https://doi.org/10.1016/j.elerap.2016.01.005
Luo, Y., Wu, C. M., y Zhang, Y. (2013). Facial expression recognition based on fusion feature of PCA and LBP with SVM. Optik, 124 (17): 2767-2770. https://doi.org/10.1016/j.ijleo.2012.08.040
Lyons, M. J. (1999). Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21 (12): 1357-1362. https://doi.org/10.1109/34.817413
Michel, A., Aleaga, L., Fernando, E., Garcés, M., y García, A. C. (2015). Sistema automatizado para la predicción de flujo de carga en subestaciones eléctricas mediante redes neuronales artificiales (Automated system for load flow prediction in power substations using artificial neural networks ), 20-35.Moreno, A.B., y Sanchez, A. (2004). GavabDB: A 3D Face Database. Proceedings 2nd COST Workshop on Biometrics on the Internet: Fundamentals, Advances and Applications. Vigo. pp. 77-82.
Moreno, A. B., Sanchez, A., Vélez, J. y Díaz, J. (2005). Face recognition using 3D local geometrical features: PCA vs. SVM. Image and Signal …, (1): 185-190. https://doi.org/10.1109/ispa.2005.195407
Olivares-Mercado, J., Toscano-Medina, K., Sánchez-Pérez, G., Nakano-Miyatake, M., y Pérez-Meana, H. (2016). Modifications to the Eigenphases Method for Face Recognition Based on SVM. Ingeniería, Investigación y Tecnología, 17 (1): 119-129. https://doi.org/10.1016/j.riit.2016.01.011
Peng, X. (2010). An efficient twin support vector machine for regression, PublMed, 23 (3): 365-372.
Phillips, P., Moon, H., Rizvi, S. y Rauss, P. (2000). The FERET evaluation methodology for face recognition algorithms, 22 (10): 1090-1104.
Pradhan, B. (2013). A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS, 51: 350-365.
Sim, T., Baker, S., y Bsat, M. (2003). The {CMU} pose, illuminlation, and expression database. Pami, 25 (12): 1615-1618. https://doi.org/10.1109/AFGR.2002.1004130
Spacek, L. (2008). Face recognition data ESSEX.
Stewart Bartlett, M. (1999). Face image analysis by unsupervised learning. Boston: Springer.
Valdes, M. M. L., Aleaga, A. M. L., y Vidal, G. G. (2014). Redes neuronales artificiales en la predicción de insolvencia . Un cambio de paradigma ante recetas tradicionales de prácticas empresariales (Artificial Neural Networks in the prediction of insolvency. A paradigm shift to traditional business practices recipes, 14: 38-58.
Vapnik, V. N. (1998). Statistical Learning Theory. Adaptive and Learning Systems for Signal Processing. Communications and Control, 2: 1-740. https://doi.org/10.2307/1271368
Wen, Y. (2012). An improved discriminative common vectors and support vector machine based face recognition approach. Expert Systems with Applications, 39 (4): 4628-4632. https://doi.org/10.1016/j.eswa.2011.09.119
Weyrauch, B., Heisele, B., y Blanz, V. (2004). Component-based Face Recognition with 3D Morphable Models. IEE: 0-4.
Yu, H., y Yang, J. (2001). A direct LDA algorithm for high-dimensional data—with application to face recognition. Pattern Recognition, 34 (February): 2067-2070. https://doi.org/10.1016/S0031-3203(00)00162-X
Z. Qi, Y. Tian, Y. S. (2013). Robust twin support vector machine for pattern classification, 305-316.
Zhou, X., Jiang, W., Tian, Y., y Shi, Y. (2010). Kernel subclass convex hull sample selection method for SVM on face recognition. Neurocomputing, 73 (10-12): 2234-2246. https://doi.org/10.1016/j.neucom.2010.01.008

Downloads

Published

2019-09-30

How to Cite

Cadena Moreano, J. A., La Serna Palomino, N. B., & Llano Casa, A. C. (2019). Facial recognition techniques using SVM: A comparative analysis. Enfoque UTE, 10(3), pp. 98 – 111. https://doi.org/10.29019/enfoque.v10n3.493

Issue

Vol. 10 No. 3 (2019)

Section

Computer Science, ICTs

License

Copyright (c) 2019 Enfoque UTE

Creative Commons License

This work is licensed under a Creative Commons Attribution 3.0 Unported License.

The articles and research published by the UTE University are carried out under the Open Access regime in electronic format.  This means that all content is freely available without charge to the user or his/her institution. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, without asking prior permission from the publisher or the author. This is in accordance with the BOAI definition of open access. By submitting an article to any of the scientific journals of the UTE University, the author or authors accept these conditions.

The UTE applies the Creative Commons Attribution (CC-BY) license to articles in its scientific journals. Under this open access license, as an author you agree that anyone may reuse your article in whole or in part for any purpose, free of charge, including commercial purposes. Anyone can copy, distribute or reuse the content as long as the author and original source are correctly cited. This facilitates freedom of reuse and also ensures that content can be extracted without barriers for research needs.

 

This work is licensed under a Creative Commons Attribution 3.0 International (CC BY 3.0).

 

The Enfoque UTE journal guarantees and declares that authors always retain all copyrights and full publishing rights without restrictions [© The Author(s)]. Acknowledgment (BY): Any exploitation of the work is allowed, including a commercial purpose, as well as the creation of derivative works, the distribution of which is also allowed without any restriction.