Result: Two-dimensional linear discriminant analysis of principle component vectors for face recognition

Title:
Two-dimensional linear discriminant analysis of principle component vectors for face recognition
Authors:
SANGUANSAT, Parinya, ASDORNWISED, Widhyakorn, JITAPUNKUL, Somchai, MARUKATAT, Sanparith
Source:
IEICE transactions on information and systems. 89(7):2164-2170
Publisher Information:
Oxford: Oxford University Press, 2006.
Publication Year:
2006
Physical Description:
print, 14 ref
Original Material:
INIST-CNRS
Subject Terms:
Electronics, Electronique, Computer science, Informatique, Telecommunications, Télécommunications, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Intelligence artificielle, Artificial intelligence, Reconnaissance des formes. Traitement numérique des images. Géométrie algorithmique, Pattern recognition. Digital image processing. Computational geometry, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Traitement du signal, Signal processing, Traitement des images, Image processing, Analyse composante principale, Principal component analysis, Análisis componente principal, Analyse discriminante, Discriminant analysis, Análisis discriminante, Modèle 2 dimensions, Two dimensional model, Modelo 2 dimensiones, Reconnaissance forme, Pattern recognition, Reconocimiento patrón, Reconnaissance visage, Face recognition, 2DPCA+2DLDA, face recognition, two-dimensional linear discriminant analysis (2DLDA), two-dimensional principle component analysis (2DPCA)
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Electrical Engineering, Chulalongkom University, Bangkok, Thailand
National Electronics and Computer Technology Center (NECTEC), Pathumthani, Thailand
ISSN:
0916-8532
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=17982806
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Telecommunications and information theory
Accession Number:
edscal.17982806
Database:
PASCAL Archive

Further Information

In this paper, we proposed a new Two-Dimensional Linear Discriminant Analysis (2DLDA) method, based on Two-Dimensional Principle Component Analysis (2DPCA) concept. In particular, 2D face image matrices do not need to be previously transformed into a vector. In this way, the spatial information can be preserved. Moreover, the 2DLDA also allows avoiding the Small Sample Size (SSS) problem, thus overcoming the traditional LDA. We combine 2DPCA and our proposed 2DLDA on the Two-Dimensional Linear Discriminant Analysis of principle component vectors framework. Our framework consists of two steps: first we project an input face image into the family of projected vectors via 2DPCA-based technique, second we project from these space into the classification space via 2DLDA-based technique. This does not only allows further reducing of the dimension of feature matrix but also improving the classification accuracy. Experimental results on ORL and Yale face database showed an improvement of 2DPCA-based technique over the conventional PCA technique.