Subspace transform induced robust similarity measure for facial images

Jian ZHANG; Heng ZHANG; Li-ling BO; Hong-ran LI; Shuai XU; Dong-qing YUAN

doi:10.1631/FITEE.1900552

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Subspace transform induced robust similarity measure for facial images

Regular Papers | Updated：2022-05-19

- Subspace transform induced robust similarity measure for facial images
- 子空间变换诱导的稳健人脸图像相似度度量
- Frontiers of Information Technology & Electronic Engineering Vol. 21, Issue 9, Pages: 1334-1345(2020)
- Affiliations：
  
  Department of Computer Engineering, Jiangsu Ocean University, Lianyungang 222005, China
  Department of Mathematics, Jiangsu Ocean University, Lianyungang 222005, China
- Author bio：
  
  [ "Jian ZHANG, E-mail: zhangjian@jou.edu.cn" ]
  Heng ZHANG, E-mail: zhangheng@jou.edu.cn
- Funds：
- DOI：10.1631/FITEE.1900552
  CLC： TP391
- Received：11 October 2019，
  
  Revised：2020-;7-23，
  
  Published：2020-09
- Accepted：
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Jian ZHANG, Heng ZHANG, Li-ling BO, et al. Subspace transform induced robust similarity measure for facial images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1334-1345.
DOI：

Jian ZHANG, Heng ZHANG, Li-ling BO, et al. Subspace transform induced robust similarity measure for facial images[J]. Frontiers of Information Technology & Electronic Engineering, 2020, 21(9): 1334-1345. DOI： 10.1631/FITEE.1900552.

摘要

相似度度量方法在许多领域（如模式识别与机器感知）扮演着重要角色，引起国内外学者重点关注。当前，为图像构建二维稳健的相似度度量方法仍是重要研究课题。本文针对稳健人脸识别问题，基于子空间性质，提出一种有效且稳健的二维图像相似度度量方法。该方法通过线性变换与奇异值分解，度量两幅对齐人脸图像的相似度，同时消弱人脸识别过程中的干扰。展示了该方法的数学特征及度量特性，进而揭示所提方法的可行性与稳健机制。结合最近邻分类器，评估了所提方法在不同挑战下的人脸识别性能。实验结果表明所提方法在准确性和稳健性方面具有一定优势。

Abstract

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.

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Keywords

references

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