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Anatomization of the systems of dimension relaxation for facial recognition

Raha, Mayamin Hamid and Deb, Tonmoay and Rahmun, Mahieyin and Chen, Tim (2021) Anatomization of the systems of dimension relaxation for facial recognition. Intelligent Decision Technologies, 14 (4). pp. 517-527. ISSN 1872-4981. https://resolver.caltech.edu/CaltechAUTHORS:20210218-153515327

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Abstract

Face recognition is the most efficient image analysis application, and the reduction of dimensionality is an essential requirement. The curse of dimensionality occurs with the increase in dimensionality, the sample density decreases exponentially. Dimensionality Reduction is the process of taking into account the dimensionality of the feature space by obtaining a set of principal features. The purpose of this manuscript is to demonstrate a comparative study of Principal Component Analysis and Linear Discriminant Analysis methods which are two of the highly popular appearance-based face recognition projection methods. PCA creates a flat dimensional data representation that describes as much data variance as possible, while LDA finds the vectors that best discriminate between classes in the underlying space. The main idea of PCA is to transform high dimensional input space into the function space that displays the maximum variance. Traditional LDA feature selection is obtained by maximizing class differences and minimizing class distance.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.3233/idt-190120DOIArticle
Additional Information:© 2021 IOS Press. Published: 6 January 2021.
Subject Keywords:Eigenvalues, face recognition, linear discriminant analysis, principal component analysis, supervised learning
Issue or Number:4
Record Number:CaltechAUTHORS:20210218-153515327
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210218-153515327
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108109
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:18 Feb 2021 23:38
Last Modified:18 Feb 2021 23:38

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