Complete Two-Dimensional PCA for Face Recognition

Authors:
Anbang Xu;Xin Jin;Yugang Jiang;Ping Guo
Affiliations:
Beijing Normal University, China;Beijing Normal University, China;Beijing Normal University, China;Beijing Normal University, China
Venue:
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Year:
2006

Citing 0
Cited 7

An approach for directly extracting features from matrix data and its application in face recognition

Neurocomputing
Using Face Quality Ratings to Improve Real-Time Face Recognition

PSIVT '09 Proceedings of the 3rd Pacific Rim Symposium on Advances in Image and Video Technology
Iris Feature Extraction Based on the Complete 2DPCA

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part II
A feature extraction method for use with bimodal biometrics

Pattern Recognition
Real-time subspace-based background modeling using multi-channel data

ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Multilinear principal component analysis for face recognition with fewer features

Neurocomputing
A unified view of two-dimensional principal component analyses

SSPR'12/SPR'12 Proceedings of the 2012 Joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a novel method, the complete twodimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. Our experiments were performed on ORL face database, and experimental results show that the proposed method has an encouraging performance.