Dual LDA - An effective feature space reduction method for face recognition

Kucharski, K, Skarbek, W and Bober, M (2005) Dual LDA - An effective feature space reduction method for face recognition

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Abstract

Linear Discriminant Analysis (LDA) is a popular feature extraction technique that aims at creating a feature set of enhanced discriminatory power. The authors introduced a novel approach Dual LDA (DLDA) and proposed an efficient SVD-based implementation. This paper focuses on feature space reduction aspect of DLDA achieved in course of proper choice of the parameters controlling the DLDA algorithm. The comparative experiments conducted on a collection of five facial databases consisting in total of more than 10000 photos show that DLDA outperforms by a great margin the methods reducing the feature space by means of feature subset selection. © 2005 IEEE.

Item Type:	Conference or Workshop Item (UNSPECIFIED)
Divisions :	Surrey research (other units)
Authors :	Name Email ORCID Kucharski, K Skarbek, W Bober, M m.bober@surrey.ac.uk
Date :	1 December 2005
DOI :	10.1109/AVSS.2005.1577291
Depositing User :	Symplectic Elements
Date Deposited :	17 May 2017 12:11
Last Modified :	23 Jan 2020 17:40
URI:	http://epubs.surrey.ac.uk/id/eprint/834390

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