Generalized PCA face recognition by image correction and bit feature fusion

Authors:
Huiyuan Wang;Yan Leng;Zengfeng Wang;Xiaojuan Wu
Affiliations:
School of Information Science and Engineering, Shandong University, Shandong, China;School of Information Science and Engineering, Shandong University, Shandong, China;School of Information Science and Engineering, Shandong University, Shandong, China;School of Information Science and Engineering, Shandong University, Shandong, China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

Citing 5
Cited 1

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition: The Problem of Compensating for Changes in Illumination Direction

IEEE Transactions on Pattern Analysis and Machine Intelligence
Natural Image Correction by Iterative Projections to Eigenspace Constructed in Normalized Image Space

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Experiments on Eigenfaces Robustness

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Eigenfaces for recognition

Journal of Cognitive Neuroscience

Facial expression recognition with local binary pattern and Laplacian Eigenmaps

ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, two approaches to improve the illumination robustness of the face recognition algorithms are presented, that is, Symmetrical Image Correction (SIC) and Bit-Plan Feature Fusion (BPFF). SIC can reduce bright speckles and shadows caused by over lighting. BPFF constructs a new virtual face with Bit-Plan information of face images. Generalized PCA is then applied to the virtual faces to achieve face recognition. Experiments show that, the proposed combined method can reduce the sensitivity of face recognition to illuminations using fewer projection vectors than the compared approaches.