Face recognition using two-dimensional nonnegative principal component analysis

Peng Ma; Ying Qu; Dan Yang; Yongxin Ge; Xiaohong Zhang

doi:10.1117/1.JEI.21.3.033011

14 August 2012 Face recognition using two-dimensional nonnegative principal component analysis

Peng Ma, Ying Qu, Dan Yang, Yongxin Ge, Xiaohong Zhang

Author Affiliations +

Journal of Electronic Imaging, Vol. 21, Issue 3, 033011 (August 2012). https://doi.org/10.1117/1.JEI.21.3.033011

Abstract

Although two-dimensional principal component analysis (2DPCA) extracts image features directly from 2D image matrices rather than one dimensional vectors, 2DPCA is only based on the whole images to preserve total variances by maximizing the trace of feature covariance matrix. Thus, 2DPCA cannot extract localized components, which are usually important for face recognition. Inspired by nonnegative matrix factorization (NMF), which is based on localized features, we propose a novel algorithm for face recognition called two-dimensional nonnegative principal component analysis (2DNPCA) to extract localized components and maintain the maximal variance property of 2DPCA. 2DNPCA is a matrix-based algorithm to preserve the local structure of facial images and has the nonnegative constraint to learn localized components. Therefore, 2DNPCA has both advantages of 2DPCA and NMF. Furthermore, 2DNPCA solves the time-consuming problem by removing the restriction of minimizing the cost function and extracting only the base matrix. The nearest neighbor (NN) classifier and linear regression (LR) classifier are used for classification and extensive experimental results show that 2DNPCA plus NN and 2DNPCA plus LR are both very efficient approaches for face recognition.

Citation Download Citation

Peng Ma, Ying Qu, Dan Yang, Yongxin Ge, and Xiaohong Zhang "Face recognition using two-dimensional nonnegative principal component analysis," Journal of Electronic Imaging 21(3), 033011 (14 August 2012). https://doi.org/10.1117/1.JEI.21.3.033011

Published: 14 August 2012

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available