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CAIP
2005
Springer

Feature Space Reduction for Face Recognition with Dual Linear Discriminant Analysis

9 years 3 months ago
Feature Space Reduction for Face Recognition with Dual Linear Discriminant Analysis
Linear Discriminant Analysis (LDA) is widely known feature extraction technique that aims at creating a feature set of enhanced discriminatory power. It was addressed by many researchers and proved to be especially successful approach in face recognition. The authors introduced a novel approach Dual LDA (DLDA) and proposed an efficient SVD-based implementation controlled by two parameters. In this paper DLDA is analyzed from the feature space reduction point of view and the role of the parameters is explained. The comparative experiments conducted on facial database consisting of nearly 2000 individuals show superiority of this approach over class of feature selection methods that choose the features one by one relying on classic statistical measures.
Krzysztof Kucharski, Wladyslaw Skarbek, Miroslaw B
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where CAIP
Authors Krzysztof Kucharski, Wladyslaw Skarbek, Miroslaw Bober
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