A novel feature extraction approach to face recognition based on partial least squares regression

Authors:
Yuan-Yuan Wan;Ji-Xiang Du;Kang Li
Affiliations:
Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei, Anhui, China;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, HeFei, Anhui, China;School of Electrical & Electronic Engineering, Queen’s University, Belfast, UK
Venue:
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Year:
2006

Citing 2
Cited 0

Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
The equivalence of two-dimensional PCA to line-based PCA

Pattern Recognition Letters

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, partial least square (PLS) regression is firstly employed in image processing. And a new technique coined partial least squares (PLS) regression, line-based PLS, is proposed for feature extraction of the images. To test this new approach, a series of experiments were performed on the famous face image database: ORL face database. Compared with newly proposed two dimensional principal component analysis (2DPCA), it can be found that the dimension of the feature vectors of the line-based PLS is no more than half of the 2DPCA’s while the recognition rate can retain at the same high level. Thus, the feature extraction based on line-based PLS regression is a feasible and effective method.