CLC number: TP391.4
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2016-01-20
Cited: 0
Clicked: 6867
Xiao-hu Ma, Meng Yang, Zhao Zhang. Local uncorrelated local discriminant embedding for face recognition[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(3): 212-223.
@article{title="Local uncorrelated local discriminant embedding for face recognition",
author="Xiao-hu Ma, Meng Yang, Zhao Zhang",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="17",
number="3",
pages="212-223",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500255"
}
%0 Journal Article
%T Local uncorrelated local discriminant embedding for face recognition
%A Xiao-hu Ma
%A Meng Yang
%A Zhao Zhang
%J Frontiers of Information Technology & Electronic Engineering
%V 17
%N 3
%P 212-223
%@ 2095-9184
%D 2016
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500255
TY - JOUR
T1 - Local uncorrelated local discriminant embedding for face recognition
A1 - Xiao-hu Ma
A1 - Meng Yang
A1 - Zhao Zhang
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 17
IS - 3
SP - 212
EP - 223
%@ 2095-9184
Y1 - 2016
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500255
Abstract: The feature extraction algorithm plays an important role in face recognition. However, the extracted features also have overlapping discriminant information. A property of the statistical uncorrelated criterion is that it eliminates the redundancy among the extracted discriminant features, while many algorithms generally ignore this property. In this paper, we introduce a novel feature extraction method called local uncorrelated local discriminant embedding (LULDE). The proposed approach can be seen as an extension of a local discriminant embedding (LDE) framework in three ways. First, a new local statistical uncorrelated criterion is proposed, which effectively captures the local information of interclass and intraclass. Second, we reconstruct the affinity matrices of an intrinsic graph and a penalty graph, which are mentioned in LDE to enhance the discriminant property. Finally, it overcomes the small-sample-size problem without using principal component analysis to preprocess the original data, which avoids losing some discriminant information. Experimental results on Yale, ORL, Extended Yale B, and FERET databases demonstrate that LULDE outperforms LDE and other representative uncorrelated feature extraction methods.
This paper proposed a local uncorrelated local discriminant embedding, coined as LULDE. Generally, this is a relatively good paper.
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