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CLC number: TP391
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2015-11-11
Cited: 3
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Face recognition based on subset selection via metric learning on manifold
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Hong Shao, Shuang Chen, Jie-yi Zhao, Wen-cheng Cui, Tian-shu Yu. Face recognition based on subset selection via metric learning on manifold[J]. Frontiers of Information Technology & Electronic Engineering, 2015, 16(12): 1046-1058.
@article{title="Face recognition based on subset selection via metric learning on manifold",
author="Hong Shao, Shuang Chen, Jie-yi Zhao, Wen-cheng Cui, Tian-shu Yu",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="16",
number="12",
pages="1046-1058",
year="2015",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1500085"
}
%0 Journal Article
%T Face recognition based on subset selection via metric learning on manifold
%A Hong Shao
%A Shuang Chen
%A Jie-yi Zhao
%A Wen-cheng Cui
%A Tian-shu Yu
%J Frontiers of Information Technology & Electronic Engineering
%V 16
%N 12
%P 1046-1058
%@ 2095-9184
%D 2015
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1500085
TY - JOUR
T1 - Face recognition based on subset selection via metric learning on manifold
A1 - Hong Shao
A1 - Shuang Chen
A1 - Jie-yi Zhao
A1 - Wen-cheng Cui
A1 - Tian-shu Yu
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 16
IS - 12
SP - 1046
EP - 1058
%@ 2095-9184
Y1 - 2015
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1500085
Abstract: With the development of face recognition using sparse representation based classification (SRC), many relevant methods have been proposed and investigated. However, when the dictionary is large and the representation is sparse, only a small proportion of the elements contributes to the l1-minimization. Under this observation, several approaches have been developed to carry out an efficient element selection procedure before SRC. In this paper, we employ a metric learning approach which helps find the active elements correctly by taking into account the interclass/intraclass relationship and manifold structure of face images. After the metric has been learned, a neighborhood graph is constructed in the projected space. A fast marching algorithm is used to rapidly select the subset from the graph, and SRC is implemented for classification. Experimental results show that our method achieves promising performance and significant efficiency enhancement.
This paper solves a practical problem in the face recognition application. The proposed framework is quite intuitive and efficient. Experiments conducted on several benchmark databases validate the effectiveness of the proposed method.
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