Application of feature extraction using nonlinear dynamic system in face recognition

被引:0
作者
Lianglei Sun
Hongchen Lin
Wanbo Yu
Yi Zhang
机构
[1] Dalian University,College of Information
来源
Evolving Systems | 2023年 / 14卷
关键词
Dynamic system; Face recognition; Chaos; Feature extraction;
D O I
暂无
中图分类号
学科分类号
摘要
This paper proposes a nonlinear dynamic system by simulating the sensitivity of V2 (second visual cortex) to the graph. The system uses the auxiliary function matrix and the target image to generate chaotic attractors with initial sensitivity, ergodicity and relatively stable similarity to extract image features. Firstly, select the excellent auxiliary function to construct auxiliary function matrix. Secondly, the target image is optimized for grayscale, and the iteration range is automatically adjusted by the Viola-Jones detector and the curvature of the image. Finally, the auxiliary function matrix and the processed target image are iteratively interleaved to generate chaotic attractors for face recognition. In this paper, we selected Yale, ORL, AR, and Jaffe face database for experiments, and the average recognition rates were 98.14%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 98.40%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, 97.06%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, and 97.74%\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\%$$\end{document}, respectively. Because of its fast speed, simple method, and large room for improvement, this method is expected to be applied in many practical fields and has theoretical value for continued research.
引用
收藏
页码:825 / 838
页数:13
相关论文
共 59 条
[1]  
Belhumeur PN(1997)Eigenfaces versus fifisherfaces: recognition using class specifific linear projection IEEE Trans Pattern Anal Mach Intell 19 711-720
[2]  
Hespanha JP(2006)Face recognition using IPCA-ICA algorithm IEEE Trans Pattern Anal Mach Intell 28 996-1000
[3]  
Kriegman DJ(2019)Some chaos notions on dendrites Symmetry 11 1309-981
[4]  
Dagher I(2013)A functional and perceptual signature of the second visual area in primates Nat Neurosci 16 974-2352
[5]  
Nachar R(2010)Push–pull marginal discriminant analysis for feature extraction Pattern Recogn Lett 31 2345-2893
[6]  
Fadel A(2012)Discriminant sparse neighborhood preserving embedding for face recognition Pattern Recogn 45 2884-130
[7]  
Dzul-Kifli Syahida C(2011)Super-resolution method for face recognition using nonlinear mappings on coherent features IEEE Trans Neural Netw 22 121-394
[8]  
Freeman J(2008)Eigenfeature regularization and extraction in face recognition IEEE Trans Pattern Anal Mach Intell 30 383-580
[9]  
Ziemba CM(2012)Non-linear factorised dynamic shape and appearance models for facial expression analysis and tracking IET Comput Vision 6 567-669
[10]  
Heeger DJ(2012)Divide, conquer and coordinate: globally coordinated switching linear dynamical system IEEE Trans Pattern Anal Mach Intell 34 654-3715