Tecnología biométrica de reconocimiento personal
DOI:
https://doi.org/10.22633/rpge.v28i00.19390Palabras clave:
Sistema de reconocimiento de objetos, Algoritmo, Visión por computadora, Tecnologías biométricasResumen
En la actualidad, el procesamiento de imágenes se utiliza ampliamente en los sistemas de seguridad para reconocer personas. Para este estudio se seleccionaron algoritmos de aprendizaje automático, en presencia de una cantidad limitada de datos. El trabajo analizó el área temática del reconocimiento facial, la relevancia de este sistema en nuestro tiempo, el reconocimiento biométrico del sistema "FaceID", varios métodos diferentes para el reconocimiento facial, e investigó en qué áreas de actividad se utiliza el sistema "Face recognition" y con qué propósito. Como parte del estudio, se analizaron varios algoritmos de reconocimiento facial. Se ha comprobado que todo el sistema de reconocimiento de caras puede modelarse utilizando el método de extracción de contornos Violy-Jones y se ha probado con un resultado de reconocimiento satisfactorio de aproximadamente el 75%. Se consideran las capacidades funcionales de la biblioteca de visión por ordenador OpenCV y de otras bibliotecas.
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AHA, D. W.; KIBLER, D.; ALBERT, M. K. Instance-based learning algorithms. Machine Learning, [S. l.], v. 6, p. 37–66, 1991. DOI: 10.1023/A:1022689900470.
BARINA, D. Gabor wavelets in image processing. In: CONFERENCE STUDENT EEICT, 17., 2011. Proceedings [...]. Brno: Brno University of Technology. 2011. v. 3, p. 522–526.
BROADLEY, C. E. Addressing the selective superiority problem: automatic algorithm/model class selection. In: MACHINE LEARNING: TENTH INTERNATIONAL CONFERENCE, Amherst, 1993. Proceedings […]. Burlington: Morgan Kaufmann, 1993. p. 17–24. DOI: 0.1016/b978-1-55860-307-3.50009-5.
DOMINGO, C.; GAVALDA, R.; WATANABE, O. Adaptive sampling methods for scaling up knowledge discovery algorithms. In: DISCOVERY SCIENCE: SECOND INTERNATIONAL CONFERENCE, 1999, Tokyo. Proceedings […]. Berlin: Springer, 1999. p. 172–183. DOI: 10.1007/3-540-46846-3_16.
ENGELBRECHT, A. Computational intelligence: an introduction. Sidney: John Wiley & Sons, 2007. 597 p. DOI: 10.1002/9780470512517.
EVANS, R. Clustering for classification: using standard clustering methods to summarise datasets with minimal loss of classification accuracy. Saarbrücken: VDM Verlag, 2008. 108 p.
GATES, G. The reduced nearest neighbor rule. IEEE Transactions on Information Theory, [S. l.], v. 18, n. 3, p. 431–433, 1972. DOI: 10.1109/TIT.1972.1054809.
HART, P. E. The condensed nearest neighbor rule. IEEE Transactions on Information Theory, [S. l.], v. 14, p. 515–516, 1968. DOI: 10.1109/TIT.1968.1054155.
JANKOWSKI, N. Data regularization. In: NEURAL NETWORKS AND SOFT COMPUTING, 5., 2000, Zakopane. Proceedings […]. Częstochowa: Polish Neural Networks Society, 2000. p. 209–214.
JANKOWSKI, N.; GROCHOWSKI, M. Comparison of instance selection algorithms I. Algorithms survey. In: ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, 7., 2004, Zakopane. Proceedings […]. Berlin: Springer, 2004. p. 598–603. (Lecture Notes in Computer Science, v. 3070). DOI: 10.1007/978-3-540-24844-6_90.
JURAFSKY, D.; MARTIN, J. Hidden Markov models. Speech and Language Processing. Draft of November 7, 2016.
KIBBLER, D.; AHA, D. W. Learning representative exemplars of concepts: an initial case of study. In: MACHINE LEARNING INTERNATIONAL WORKSHOP, 4., 1987, Irvine. Proceedings […]. Burlington: Morgan Kaufmann, 1987. p. 24–30. DOI: 10.1016/b978-0-934613-41-5.50006-4.
KOHONEN, T. Learning vector quantization. Neural Networks, [S. l.], v. 1, p. 303, 1988. DOI: 10.1016/0893-6080(88)90334-6.
KOSKIMAKI, H.; JUUTILAINEN, I.; LAURINEN, P.; RÖNING, J. Two-level clustering approach to training data instance selection: a case study for the steel industry. In: NEURAL NETWORKS: INTERNATIONAL JOINT CONFERENCE, 2008, Hong Kong. Proceedings […]. Los Alamitos: IEEE, 2008. p. 3044–3049. DOI: 10.1109/ijcnn.2008.4634228.
LI, B.; CHI, M.; FAN, J.; XUE, X. Support cluster machine. In: MACHINE LEARNING INTERNATIONAL CONFERENCE, 24., 2007, Corvallis. Proceedings […]. New York, 2007. p. 505–512. DOI: 10.1145/1273496.1273560.
MADIGAN, D.; RAGHAVAN, N.; DUMOUCHEL, W.; NASON, M. Likelihood-based data squashing: a modeling approach to instance construction. Data Mining and Knowledge Discovery, [S. l.], v. 6, n. 2. p. 173–190. DOI: 10.1023/A:1014095614948.
REEVES, C. R.; BUSH, D. R. Using genetic algorithms for training data selection in RBF networks. In: Instance Selection and Construction for Data Mining. Norwell: Kluwer, 2001. Part VI. p. 339–356. DOI: 10.1007/978-1-4757-3359-4_19.
REINARTZ, T. A. Unifying view on instance selection. Data Mining and Knowledge Discovery, [S. l.], v. 6, p. 191–210, 2002. DOI: 10.1023/A:1014047731786.
RITTER, G.; WOODRUFF, H.; LOWRY, S.; ISENHOUR, T. An algorithm for a selective nearest neighbor decision rule. IEEE Transactions on Information Theory, [S. l.], v. 21, n. 6, p. 665–669, 1975. DOI: 10.1109/TIT.1975.1055464.
SANE, S. S.; GHATOL, A. A. A Novel supervised instance selection algorithm. International Journal of Business Intelligence and Data Mining, [S. l.], v. 2, n 4. p. 471–495, 2007. DOI: 10.1504/IJBIDM.2007.016384.
SKALAK, D. B. Prototype and feature selection by sampling and random mutation hill climbing algorithms. In: MACHINE LEARNING: INTERNATIONAL CONFERENCE, 11., 1994, New Brunswick. Proceedings […]. Burlington: Morgan Kaufmann, 1994. p. 293–301. DOI: 10.1016/b978-1-55860-335-6.50043-x.
SUBBOTIN, S. The neuro-fuzzy network synthesis and simplification on precedents in problems of diagnosis and pattern recognition. Optical Memory and Neural Networks (Information Optics), [S. l.], v. 22, n. 2, p. 97–103, 2013a. DOI: 10.3103/s1060992x13020082.
SUBBOTIN, S. Methods of sampling based on exhaustive and evolutionary search. Automatic Control and Computer Sciences, [S. l.], v. 47, n. 3, p. 113–121, 2013b. DOI: 10.3103/s0146411613030073.
SUYKENS, J. A.; VANDEWALLE, J. Least squares support vector machine classifiers. Neural Processing Letters, [S. l.], v. 9, n 3. p. 293–300, 1999. DOI: 10.1023/A:1018628609742.
TOMEK, I. An experiment with the edited nearest-neighbor rule. IEEE Transactions on Systems, Man and Cybernetics, [S. l.],v. 6, p. 448–452, 1976. DOI: 10.1109/TSMC.1976.4309523.
WILSON, D. L. Asymptotic properties of nearest neighbor rules using edited data. IEEE Transactions on Systems, Man, Cybernetics, [S. l.], v. 2, n. 3, p. 408–421, 1972. DOI: 10.1109/TSMC.1972.4309137.
WILSON, D. R.; Martinez, D. R. Reduction techniques for instancebased learning algorithms. Machine Learning, [S. l.],v. 38, n. 3, p. 257–286, 2000. DOI: 10.1023/A:1007626913721.
WINARNO, E.; HADIKURNIAWATI, W.; NIRWANTO, A.; ABDULLAH, D. Multi-view faces detection using viola-jones method. Journal of Physics Conference Series, [S. l.], v. 1114, n. 1, 2018. DOI: 10.1088/1742-6596/1114/1/012068.
YOON, B. J. Hidden Markov models and their application in the analysis of biological sequences. Current Genomics, [S. l.], v. 10, n. 6, p. 402–415, 2009. DOI: 10.2174/138920209789177575.
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