Spectral Regression dimension reduction for multiple features facial image retrieval

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Title Spectral Regression dimension reduction for multiple features facial image retrieval
Author Zhang, Bailing; Gao, Yongsheng
Journal Name International Journal of Biometrics
Year Published 2012
Place of publication United Kingdom
Publisher Inderscience Publishers
Abstract Face retrieval has received much attention in recent years. This paper comparatively studied five feature description methods for face representation, including Local Binary Pattern (LBP), Gabor feature, Gray Level Co-occurrence Matrices (GLCM), Pyramid Histogram of Oriented Gradient (PHOG) and Curvelet Transform (CT). The problem of large dimensionalities of the extracted features was addressed by employing a manifold learning method called Spectral Regression (SR). A fusion scheme was proposed by aggregating the distance metrics. Experiments illustrated that dimension reduced features are more efficient and the fusion scheme can offer much enhanced performance. A 98% rank 1 accuracy was obtained for the AR faces and 92% for the FERET faces.
Peer Reviewed Yes
Published Yes
Alternative URI http://dx.doi.org/10.1504/IJBM.2012.044296
Volume 4
Issue Number 1
Page from 77
Page to 101
ISSN 1755-8301
Date Accessioned 2012-06-18
Language en_US
Research Centre Institute for Integrated and Intelligent Systems
Faculty Faculty of Science, Environment, Engineering and Technology
Subject Computer Vision; Pattern Recognition and Data Mining
URI http://hdl.handle.net/10072/47170
Publication Type Journal Articles (Refereed Article)
Publication Type Code c1

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