Open AccessImproved complete neighbourhood preserving embedding for face recognition
Author(s) -
Lu GuiFu,
Wang Yong,
Zou Jian
Publication year - 2013
Publication title -
iet computer vision
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 37
eISSN - 1751-9640
pISSN - 1751-9632
DOI - 10.1049/iet-cvi.2012.0202
Subject(s) - neighbourhood (mathematics) , embedding , degenerate energy levels , eigenvalues and eigenvectors , oracle , facial recognition system , face (sociological concept) , computer science , mathematics , artificial intelligence , pattern recognition (psychology) , mathematical analysis , social science , physics , software engineering , quantum mechanics , sociology
Complete neighbourhood preserving embedding (CNPE) is a recently proposed approach to overcome the drawbacks of neighbourhood preserving embedding (NPE) which is difficult to directly apply to face recognition because of computational complexity. However, there are still disadvantages for CNPE: (i) CNPE is time‐consuming when N is large, here N is the sample size; (ii) the solutions of CNPE may suffer from the degenerate eigenvalue problem, that is, several eigenvectors with the same maximal eigenvalue, which make them not optimal in terms of the discriminant ability. In this study, the authors proposed a new approach, namely improved complete neighbourhood preserving (ICNPE), to address the drawbacks of CNPE. ICNPE is more efficient than CNPE and can overcome the degenerate eigenvalue problem of CNPE. Experiments on the Olivetti & Oracle Research Laboratory (ORL), Yale, PIE (pose, illumination and expression) and Alex Martinez and Robert Benavente (AR) face databases show the effectiveness of the proposed ICNPE.