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Cluster Computing

, Volume 22, Supplement 4, pp 9397–9406 | Cite as

Null-space based facial classifier using linear regression and discriminant analysis method

  • D. Venkata Vara PrasadEmail author
  • Suresh Jaganathan
Article
  • 101 Downloads

Abstract

In this paper, we proposed a novel classification method for face recognition which adopts the functionalities of linear discriminant and regression. Linear discriminant and regression analysis methods have benefits regarding minimising time, memory usage and better feature extraction. Linear regression and discriminant classification (LRDC) makes use of the principle that a sample class lie in a linear subspace, proposed method represents a predicted image as a linear combination of class-specific galleries. LRDC belongs to the category of nearest subspace classification and finds the set of optimal discriminant projection vectors by adopting singular value decomposition (SVD) and null space, and the decision made for a class with the minimum distance. LRDC is extensively evaluated by applying it to different classified datasets and compared with the state-of-the-art algorithms.

Keywords

Classification Discriminant analysis Regression analysis Small sample size Face recognition Null space 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringSSN College of EngineeringChennaiIndia

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