Biometric technology of personal recognition
DOI:
https://doi.org/10.22633/rpge.v28i00.19390Keywords:
Object recognition system, Algorithm, Computer vision, Biometric technologiesAbstract
Today, image processing is widely used in security systems to recognize people. For this study, machine learning algorithms were selected, in the presence of a limited amount of data. The work analyzed the subject area of face recognition, the relevance of this system in our time, biometric recognition of the «FaceID» system, several different methods for face recognition, and investigated in which areas of activity the «Face recognition» system is used for which purpose. As part of the study, a number of face recognition algorithms were analyzed. It has been proven that the entire face recognition system can be modeled using the Violy-Jones contour extraction method and tested with a successful recognition result of approximately 75%. The functional capabilities of the OpenCV computer vision library and other libraries are considered.
Downloads
References
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.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Revista on line de Política e Gestão Educacional
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Manuscritos aceitos e publicados são de propriedade da Revista on line de Política e Gestão Educacional. É vedada a submissão integral ou parcial do manuscrito a qualquer outro periódico. A responsabilidade do conteúdo dos artigos é exclusiva dos autores. É vedada a tradução para outro idioma sem a autorização escrita do Editor ouvida a Comissão Editorial Científica.
