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Cluster Computing

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24-02-2018

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

Authors: D. Venkata Vara Prasad, Suresh Jaganathan

Published in: Cluster Computing | Special Issue 4/2019

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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.

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Title: Null-space based facial classifier using linear regression and discriminant analysis method
Authors: D. Venkata Vara Prasad
Suresh Jaganathan
Publication date: 24-02-2018
Publisher: Springer US
Published in: Cluster Computing / Issue Special Issue 4/2019
Print ISSN: 1386-7857
Electronic ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-018-2178-z

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