Paper Info
Title
Using Signal/Residual Information of Eigenfaces for PCA Face Space Dimensionality Characteristics
Abstract
Principal Component Analysis has been used since 1990 [1] in many recognition algorithms to get a face feature representation and to exploit the dimensionality reduction characteristic of the Principal Component Analysis (PCA). The way to determine the optimal dimension of the reduced space is still not available. Another critical point when working with PCA is the influence of the training set, denoted here as PCA construction set. In this paper we are working on the behaviour of the signal/residual information of the PCAeigenspectrum in order to determine an optimal threshold that could be used for the dimensionality reduction. We also study the influence of different sets used to construct the PCA representation. Our experiments are done on the FRGCv21 database, using the BEE PCA baseline software. We also use images from the BANCA database for the construction of the PCA respresentations.
Year
DOI
Venue
2006
10.1109/ICPR.2006.1155
Pattern Recognition, 2006. ICPR 2006. 18th International Conference
Keywords
Field
DocType
biometrics (access control),eigenvalues and eigenfunctions,face recognition,image representation,principal component analysis,dimensionality reduction,eigenfaces,eigenspectrum,face feature representation,face recognition,face space dimensionality,image representation,optimal threshold,principal component analysis,residual information,signal information
Facial recognition system,Residual,Computer vision,Sparse PCA,Eigenface,Dimensionality reduction,Pattern recognition,Computer science,Curse of dimensionality,Software,Artificial intelligence,Principal component analysis
Conference
Volume
ISSN
ISBN
4
1051-4651
0-7695-2521-0
Citations 
PageRank 
References 
2
0.39
2
Authors
3
Name
Order
Citations
PageRank
M. Anouar Mellakh120.39
Dijana Petrovska-Delacretaz2576.98
Bernadette Dorizzi3103882.70