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Face Recognition Systems in Math Classroom Through Computer Vision Traditional Techniques

  • Luis GrandaEmail author
  • Luis Barba-GuamanEmail author
  • Pablo Torres-CarriónEmail author
  • Jorge CorderoEmail author
Conference paper
  • 59 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1194)

Abstract

The methods and techniques of detection of the human face and facial recognition have presented a great impulse in recent years, thanks to the advance in areas such as artificial vision and machine learning. Although deep neural network techniques are in vogue, traditional techniques allow you to create applications that do not consume many resources from computing devices. In this research, we present a facial recognition system that implements the Eigenfaces method, developed in C # of Microsoft Visual Studio and open-source video processing libraries such as OpenCV as EmguCV. The application is divided into two sections: the first called register where, through an integrated camera, images of the user’s face or other means such as video and stored images are captured, and the second section is known as recognition where the user is compared with all the records of the data set, indicating whether this is registered and the recognition percentage. The project was implemented with a universe of the size of twenty-five users, of which six are men (24%) and nineteen are women (76%), developing tests for five weeks.

Keywords

Facial recognition Computer vision Eigenfaces Image processing 

References

  1. 1.
    Gao, Y., Qi, Y.: Robust visual similarity retrieval in single model face databases. Pattern Recogn. 38(7), 1009–1020 (2005)CrossRefGoogle Scholar
  2. 2.
    Kaushik, S., Dubey, R.B., Madan, A.: Study of face recognition techniques. Int. J. Adv. Comput. Res. (2014)Google Scholar
  3. 3.
    Lu, X.: Image analysis for face recognition. Pers. Notes 5, 1–37 (2003)Google Scholar
  4. 4.
    Pietikäinen, M., Hadid, A., Zhao, G., Ahonen, T.: Computer Vision Using Local Binary Patterns, vol. 40. Springer, London (2011).  https://doi.org/10.1007/978-0-85729-748-8_14CrossRefGoogle Scholar
  5. 5.
    Parker, R.E.: Picture processing during recognition. J. Exp. Psychol. Hum. Percept. Perform. 4(2), 284–293 (1978)CrossRefGoogle Scholar
  6. 6.
    Cama Castillo, Y.A.: Prototipo computacional para la detección y clasificación de expresiones faciales mediante la extracción de patrones locales binarios, pp. 1–94 (2015)Google Scholar
  7. 7.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  8. 8.
    Huang, D., Shan, C., Ardabilian, M., Wang, Y., Chen, L.: Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)CrossRefGoogle Scholar
  9. 9.
    Smiatacz, M.: Eigenfaces, Fisherfaces, Laplacianfaces, Marginfaces - how to face the face verification task. Adv. Intell. Syst. Comput. 226, 187–196 (2013)Google Scholar
  10. 10.
    Face Recognition with OpenCV – OpenCV 2.4.13.6 documentation. https://docs.opencv.org/2.4/modules/contrib/doc/facerec/facerec_tutorial.htmllast. Accessed 01 Sept 2019
  11. 11.
    Sirovich, L., Kirby, M.: Low-dimensional procedure for the characterization of human faces. J. Opt. Soc. Am. A 4(3), 519 (1987)CrossRefGoogle Scholar
  12. 12.
    Gomathi, E., Baskaran, K.: Recognition of faces using improved principal component analysis. In: The 2nd International Conference on Machine Learning and Computing, ICMLC 2010, pp. 198–201 (2010)Google Scholar
  13. 13.
    Barba Guamán, L.: Utilización de métodos de visión artificial para PC como apoyo en la automoción. Dissertation, ETSI_Sistemas_Infor (2015)Google Scholar
  14. 14.
    Rosebrock, A.: Deep Learning for Computer Vision with Python: Starter Bundle. PyImageSearch (2017)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Grupo de Investigación Inteligencia Artificial AplicadaUniversidad Técnica Particular de LojaLojaEcuador

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