Comparison of Various Face Recognition Techniques in Modelling Associations of Discriminant Factors

  • T. Vijayakumar
  • B. Kedarnath
  • Achampet Harshavardhan
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


As Future is of Artificial Intelligence, which will assume a noteworthy part in each stream. One of it is Face Recognition. In this paper we separate diverse kinds of algorithms utilized as a part of face acknowledgment in future in given amount of time. To check the developing effect of Artificial knowledge on facial acknowledgment innovation crosswise over fields like Security, Healthcare and Marketing. The MUCT database consists of 3755 faces out of with 76 manual data. The database was created to provide more diversity of lighting, age, and ethnicity than currently available landmarked 2D faces Databases. The MUCT database was prepared by Stephen Milborrow, John Morkel, and Fred Nicolls in December 2008 at the University Of Cape Town. LBP yields clearly higher speed rates than the Viola Jones algorithms but accuracy is decreased by 10–20% in all the FERET test sets and in the statistical test [19]. It is our expectation that by inspecting the numerous current calculations, we will see far and away superior speed calculations created to take care of this basic computer vision issue.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • T. Vijayakumar
    • 1
  • B. Kedarnath
    • 1
  • Achampet Harshavardhan
    • 1
  1. 1.Guru Nanak Institution of TechnologyHyderabadIndia

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