Open AccessOrthogonal enhanced linear discriminant analysis for face recognition
Author(s) -
Lin Chuang,
Wang Binghui,
Fan Xin,
Ma Yanchun,
Liu Huiyun
Publication year - 2016
Publication title -
iet biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 28
eISSN - 2047-4946
pISSN - 2047-4938
DOI - 10.1049/iet-bmt.2014.0086
Subject(s) - computer science , linear discriminant analysis , pattern recognition (psychology) , artificial intelligence , facial recognition system , discriminative model , dimensionality reduction , orthogonality , orthogonal basis , principal component analysis , face (sociological concept) , mathematics , social science , physics , geometry , quantum mechanics , sociology
From the intuition that natural face images lie on or near a low‐dimensional submanifold, the authors propose a novel spectral graph based dimensionality reduction method, named orthogonal enhanced linear discriminant analysis (OELDA), for face recognition. OELDA is based on enhanced LDA (ELDA), which takes into account both the discriminative structure and geometrical structure of the face space, and generates non‐orthogonal basis vectors. However, a significant fact is that eliminating the dependence of basis vectors can promote more effective recognition of unseen face images. For this purpose, the authors seek to improve the ELDA scheme by imposing orthogonal constraints on the basis vectors. Experimental results on real‐world face datasets show that, benefitting from orthogonality, OELDA has more locality preserving power and discriminative power than LDA and ELDA, and achieves the highest recognition rates among compared methods.