Combined Fisherfaces framework

In this paper, a Complex LDA based combined Fisherfaces framework, coined Complex Fisherfaces, is developed for face feature extraction and recognition. In this framework, Principal Component Analysis (PCA) and Kernel PCA (KPCA) are first used for feature extraction. Then, the resulting PCA-based li...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Image and vision computing Ročník 21; číslo 12; s. 1037 - 1044
Hlavní autoři: Yang, Jian, Yang, Jing-yu, Frangi, Alejandro F.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier B.V 01.11.2003
Témata:
Eigenfaces
Face recognition
Feature extraction
Fisher linear discriminant analysis
Fisherfaces
Kernel based methods
Principal component analysis
Eigenfaces
Kernel based methods
Face recognition
Fisher linear discriminant analysis
Fisherfaces
Feature extraction
Principal component analysis
ISSN:0262-8856, 1872-8138
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:In this paper, a Complex LDA based combined Fisherfaces framework, coined Complex Fisherfaces, is developed for face feature extraction and recognition. In this framework, Principal Component Analysis (PCA) and Kernel PCA (KPCA) are first used for feature extraction. Then, the resulting PCA-based linear features and KPCA-based nonlinear features are integrated by complex vectors and, Complex LDA is further employed for feature fusion. The proposed method is tested on a subset of FERET database. The experimental results demonstrate that Complex Fisherfaces outperforms Fisherfaces and Kernel Fisherfaces. Also, the complex vector based parallel feature fusion strategy is demonstrated to be much more effective and robust than the super-vector based serial feature fusion strategy for face recognition.
ISSN:0262-8856
1872-8138
DOI:10.1016/j.imavis.2003.07.005