Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition

Parinya SANGUANSAT; Widhyakorn ASDORNWISED; Somchai JITAPUNKUL; Sanparith MARUKATAT

doi:10.1093/ietisy/e89-d.7.2164

Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition

Parinya SANGUANSAT, Widhyakorn ASDORNWISED, Somchai JITAPUNKUL, Sanparith MARUKATAT

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E89-D No.7 pp.2164-2170

Publication Date: 2006/07/01

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Online ISSN: 1745-1361

DOI: 10.1093/ietisy/e89-d.7.2164

Type of Manuscript: Special Section PAPER (Special Section on Machine Vision Applications)

Category: Face, Gesture, and Action Recognition

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Parinya SANGUANSAT, Widhyakorn ASDORNWISED, Somchai JITAPUNKUL, Sanparith MARUKATAT, "Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition" in IEICE TRANSACTIONS on Information, vol. E89-D, no. 7, pp. 2164-2170, July 2006, doi: 10.1093/ietisy/e89-d.7.2164.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e89-d.7.2164/_p

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@ARTICLE{e89-d_7_2164,
author={Parinya SANGUANSAT, Widhyakorn ASDORNWISED, Somchai JITAPUNKUL, Sanparith MARUKATAT, },
journal={IEICE TRANSACTIONS on Information},
title={Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition},
year={2006},
volume={E89-D},
number={7},
pages={2164-2170},
abstract={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.},
keywords={},
doi={10.1093/ietisy/e89-d.7.2164},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - Two-Dimensional Linear Discriminant Analysis of Principle Component Vectors for Face Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 2164
EP - 2170
AU - Parinya SANGUANSAT
AU - Widhyakorn ASDORNWISED
AU - Somchai JITAPUNKUL
AU - Sanparith MARUKATAT
PY - 2006
DO - 10.1093/ietisy/e89-d.7.2164
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E89-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2006
AB - 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.
ER -