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Application of feature extraction using nonlinear dynamic system in face recognition

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Abstract

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\(\%\), 98.40\(\%\), 97.06\(\%\), and 97.74\(\%\), 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.

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Data availability

The data that support the findings of this study are available from the corresponding author, [Lianglei Sun], upon reasonable request.

References

  • Belhumeur PN, Hespanha JP, Kriegman DJ (1997) Eigenfaces versus fifisherfaces: recognition using class specifific linear projection. IEEE Trans Pattern Anal Mach Intell 19(7):711–720

    Article  Google Scholar 

  • Chen D, Cao L (2011) Face recognition based on multi-module singular value features and probabilistic subspaces analysis. In: 4th International Congress on Image and Signal Processing. IEEE 2011(3):1508–1512

  • Dagher I, Nachar R (2006) Face recognition using IPCA-ICA algorithm. IEEE Trans Pattern Anal Mach Intell 28(6):996–1000

    Article  Google Scholar 

  • Ennehar CB (2017) New face features to detect multiple faces in complex background. Evolving Syst

  • Fadel A, Dzul-Kifli Syahida C (2019) Some chaos notions on dendrites. Symmetry 11(10):1309

    Article  Google Scholar 

  • Freeman J, Ziemba CM, Heeger DJ et al (2013) A functional and perceptual signature of the second visual area in primates. Nat Neurosci 16(7):974–981

    Article  Google Scholar 

  • Gu Z, Yang J, Zhang L (2010) Push–pull marginal discriminant analysis for feature extraction. Pattern Recogn Lett 31(15):2345–2352

    Article  Google Scholar 

  • Gui J, Sun Z, Jia W et al (2012) Discriminant sparse neighborhood preserving embedding for face recognition. Pattern Recogn 45(8):2884–2893

    Article  MATH  Google Scholar 

  • He X, Cai D, Yan S, Zhang H (2005) Neighborhood preserving embedding. In: Proceedings of IEEE conference on computer vision (ICCV2005), pp 1208–1213

  • Huang H, He HT (2011) Super-resolution method for face recognition using nonlinear mappings on coherent features. IEEE Trans Neural Netw 22(1):121–130

    Article  Google Scholar 

  • Jiang X, Mandal B, Kot A (2008) Eigenfeature regularization and extraction in face recognition. IEEE Trans Pattern Anal Mach Intell 30(3):383–394

    Article  Google Scholar 

  • Jiang C, Wang M, Tang X et al (2019) Face recognition method based on sparse representation and feature fusion. Chinese Automation Congress (CAC), IEEE 2019:396–400

  • Kamarajugadda KK, Polipalli TR (2018) Stride towards aging problem in face recognition by applying hybrid local feature descriptors. Evolving Syst

  • Kasemsumran P, Auephanwiriyakul S, Theera-Umpon N (2016) Face recognition using string grammar fuzzy K-nearest neighbor. In: 8th International Conference on Knowledge and Smart Technology (KST). IEEE: 55–59

  • Lee CS, Elgammal A (2012) Non-linear factorised dynamic shape and appearance models for facial expression analysis and tracking. IET Comput Vision 6(6):567–580

    Article  Google Scholar 

  • Li R, Tian TP, Sclaroff S (2012) Divide, conquer and coordinate: globally coordinated switching linear dynamical system. IEEE Trans Pattern Anal Mach Intell 34(4):654–669

    Article  Google Scholar 

  • Long M, Wang L (2021) S-box design based on discrete chaotic map and improved artificial bee colony algorithm. IEEE Access

  • Lu XJ et al (2021) Detection and classification of epileptic EEG signals by the methods of nonlinear dynamics. Chaos Solit Fract 151:111032

    Article  MathSciNet  Google Scholar 

  • Roe AW, Ts’o DY (1995) Visual topography in primate V2: multiple representation across functional stripes. J Neurosci 15(5):3689–3715

    Article  Google Scholar 

  • Sadeghi H, Raie AA (2017) Approximated Chi-square distance for histogram matching in facial image analysis: face and expression recognition. In: 10th Iranian Conference on Machine Vision and Image Processing (MVIP). IEEE pp. 188–191

  • Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vision 57(2):137–154

    Article  Google Scholar 

  • Wanbo Y (2017) Application of chaos in image processing and recognition. In: International Conference on Computer Systems, Electronics and Control (ICCSEC), IEEE, 2017:1108–1113

  • Wanbo Y, Bin Z (2014) A new chaotic attractor graphics drawing method based on the curved iteration. Acta Phys Sin 63(12):120502

  • Wanbo Y, Daqing W (2015) Image chaotic characteristics and application in face recognition. J Comput-Aided Des Comput Graph 27(12):2264–2271

    Google Scholar 

  • Wanbo Y, Hao W (2021) Analysis of trigonometric chaotic sequence by proposing an index-based bit level scrambling image encryption. Mod Phys Lett B 35(24):2150406

    Article  MathSciNet  Google Scholar 

  • Wanbo Y, Qingtao F (2020) Feature extraction of video data based on trigonometric function iteration. J Graph 41(4):512–519 ((in Chinese))

    Google Scholar 

  • Wanbo Y, Ting Y (2020) Analysis of chaotic characteristics of trigonometric function system. Mod Phys Lett B 34(21):2050210

    Article  MathSciNet  Google Scholar 

  • Wanbo Y, Xiangxiang W, Daqing W (2020) Face image recognition based on iteration of discrete cosine transform basis functions. J Graph 41(1):88–92 ((in Chinese))

    Google Scholar 

  • Wanbo Y, Shuo Y, Hongyue Y (2017) Varied cycles of the dynamic system constructed of DCT basis function and image. In: 2017 First International conference on electronics instrumentation & information systems (EIIS). IEEE, 1–6

  • Wang G, Shi N (2020) Collaborative representation-based discriminant neighborhood projections for face recognition. Neural Comput Appl 32(10):5815–5832

    Article  Google Scholar 

  • Wang J, Ma Z, Zhang B et al (2011) A structure-preserved local matching approach for face recognition. Pattern Recogn Lett 32(3):494–504

    Article  Google Scholar 

  • Yang J, Zhang D, Frangi AF et al (2004) Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans Pattern Anal Mach Intell 26(1):131–137

    Article  Google Scholar 

  • Yang W, Wang Z, Sun C (2015) A collaborative representation based projections method for feature extraction. Pattern Recogn 48(1):20–27

    Article  Google Scholar 

  • Yin J, Wei L, Song M et al (2016) Optimized projection for collaborative representation based classification and its applications to face recognition. Pattern Recogn Lett 73:83–90

    Article  Google Scholar 

  • Zang F, Zhang J, Pan J (2012) Face recognition using elastic faces. Pattern Recogn 45(11):3866–3876

    Article  MATH  Google Scholar 

  • Zhao D, Lin Z, Tang X (2007) Laplacian PCA and its applications. In: 2007 IEEE 11th International Conference on Computer Vision. IEEE, pp 1–8

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Authors and Affiliations

  1. College of Information, Dalian University, Dalian, China

    Lianglei Sun, Hongchen Lin, Wanbo Yu & Yi Zhang

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  1. Lianglei Sun
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  2. Hongchen Lin
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  3. Wanbo Yu
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  4. Yi Zhang
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All authors contributed equally to this paper.

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Correspondence to Lianglei Sun.

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Sun, L., Lin, H., Yu, W. et al. Application of feature extraction using nonlinear dynamic system in face recognition. Evolving Systems 14, 825–838 (2023). https://doi.org/10.1007/s12530-022-09468-8

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  • DOI: https://doi.org/10.1007/s12530-022-09468-8

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