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CLC number: TP391

On-line Access: 2020-09-09

Received: 2019-10-11

Revision Accepted: 2020-03-23

Crosschecked: 2020-07-23

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Citations:  Bibtex RefMan EndNote GB/T7714


Jian Zhang


Heng Zhang


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Frontiers of Information Technology & Electronic Engineering  2020 Vol.21 No.9 P.1334-1345


Subspace transform induced robust similarity measure for facial images

Author(s):  Jian Zhang, Heng Zhang, Li-ling Bo, Hong-ran Li, Shuai Xu, Dong-qing Yuan

Affiliation(s):  Department of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China; more

Corresponding email(s):   zhangjian@jou.edu.cn, zhangheng@jou.edu.cn

Key Words:  Subspace analysis, Image similarity measure, Face recognition, Pattern recognition

Jian Zhang, Heng Zhang, Li-ling Bo, Hong-ran Li, Shuai Xu, Dong-qing Yuan. Subspace transform induced robust similarity measure for facial images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1334-1345.

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author="Jian Zhang, Heng Zhang, Li-ling Bo, Hong-ran Li, Shuai Xu, Dong-qing Yuan",
journal="Frontiers of Information Technology & Electronic Engineering",
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%T Subspace transform induced robust similarity measure for facial images
%A Jian Zhang
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%A Li-ling Bo
%A Hong-ran Li
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%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1900552

T1 - Subspace transform induced robust similarity measure for facial images
A1 - Jian Zhang
A1 - Heng Zhang
A1 - Li-ling Bo
A1 - Hong-ran Li
A1 - Shuai Xu
A1 - Dong-qing Yuan
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 21
IS - 9
SP - 1334
EP - 1345
%@ 2095-9184
Y1 - 2020
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1900552

Similarity measure has long played a critical role and attracted great interest in various areas such as pattern recognition and machine perception. Nevertheless, there remains the issue of developing an efficient two-dimensional (2D) robust similarity measure method for images. Inspired by the properties of subspace, we develop an effective 2D image similarity measure technique, named transformation similarity measure (TSM), for robust face recognition. Specifically, the TSM method robustly determines the similarity between two well-aligned frontal facial images while weakening interference in the face recognition by linear transformation and singular value decomposition. We present the mathematical features and some odds to reveal the feasible and robust measure mechanism of TSM. The performance of the TSM method, combined with the nearest neighbor rule, is evaluated in face recognition under different challenges. Experimental results clearly show the advantages of the TSM method in terms of accuracy and robustness.





Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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