nach oben

Erschienen in:

2020 | OriginalPaper | Buchkapitel

Role of Distance Measures in Approximate String Matching Algorithms for Face Recognition System

verfasst von : B. Krishnaveni, S. Sridhar

Erschienen in: Computational Intelligence in Data Science

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper is based on the recognition of faces using string matching. The approximate string matching is a method for finding an approximate match of a pattern within a string. Exact matching is impracticable for a larger amount of data as it involves more time. Those issues can be solved by finding an approximate match rather than an exact match. This paper aims to experiment with the performance of approximation string matching approaches using various distance measures such as Edit distance, Longest Common Subsequence (LCSS), Hamming distance, Jaro distance, and Jaro-Winkler distance. The algorithms generate a near-optimal solution to face recognition system with reduced computational complexity. This paper deals with the conversion of face images into strings, matching those image strings by using the approximation string matching algorithm that determines the distance and classifies a face image based on the minimum distance. Experiments have been performed with FEI and ORL face databases for the evaluation of approximation string matching algorithms and the results demonstrate the utility of distance measures for the face recognition system.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel A Secure Blind Watermarking Scheme Using Wavelets, Arnold Transform and QR Decomposition

Nächstes Kapitel Detection of Human Faces in Video Sequences Using Mean of GLBP Signatures

Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.: Face recognition: a literature survey. ACM Comput. Surv. 35, 399–458 (2003)

Kirby, M., Sirovich, L.: Application of the Karhunen-Loève procedure for the characterization of the human face. IEEE Trans. Pattern Anal. Mach. Intell. 12, 103–108 (1990)CrossRef

Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the IEEE Conference on CVPR, pp. 586–591 (1991)

Yu, H., Yang, J.: A direct LDA algorithm for high-dimensional data with application to face recognition. Pattern Recognit. 34, 2067–2070 (2001)CrossRef

Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fishy faces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19, 711–720 (1997)

Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13, 1450–1464 (2002)CrossRef

Miar-Naimi, H., Davari, P.: A new fast and efficient HMM-based face recognition system using a 7-state HMM along with SVD coefficients. Iran. J. Electr. Electron. Eng. 4 (2008)

Geng, X., Zhou, Z., Smith-Miles, K.: Individual stable space: an approach to face recognition under uncontrolled conditions. IEEE Trans. Neural Netw. 19(8), 1354–1368 (2008)CrossRef

Gaston, J., Ming, J., Crookes, D.: Matching larger image areas for unconstrained face identification. IEEE Trans. Cybern. 49(8), 3191–3202 (2019)

10.

Qiangchang, W., Guodong, G.: LS-CNN: characterizing local patches at multiple scales for face recognition. IEEE Trans. Inf. Forensics Secur. 15, 1640–1652 (2020)CrossRef

11.

Alhendawi, K.M.A., Baharudin, S.: String matching algorithms (SMAs): survey & empirical analysis. J. Comput. Sci. Manag. 2(5), 2637–2644 (2013)

12.

Navarro, G.: A guided tour to approximate string matching. ACM Comput. Surv. 33(1), 31–88 (2001)CrossRef

13.

Ukkonen, E.: Algorithms for approximate string matching. Inf. Control 64(1–3), 100–118 (1985)MathSciNetCrossRef

14.

Williamson, D.P., Shmoys, D.B.: The Design of Approximation Algorithms, 1 edn. Cambridge University Press, Cambridge (2011)

15.

Masek, W.J., Paterson, M.: A faster algorithm computing string edit distances. J. Comput. Syst. Sci. 20(1), 18–31 (1980)MathSciNetCrossRef

16.

Aho, A.V., Hopcroft, J.E., Ullman, J.D.: The Design and Analysis of Computer Algorithms. Addison-Wesley, Reading (1974)

17.

Hamming, R.W.: Error detecting and error-correcting codes. Bell Syst. Tech. J. 29(2), 147–160 (1950)MathSciNetCrossRef

18.

Jaro, M. A.: Advances in record linkage methodology as applied to the 1985 census of Tampa Florida. J. Am. Stat. Assoc. 84(406), 414–420 (1989)

19.

Winkler, W.E.: Overview of record linkage and current research directions (PDF). Research Report Series, RRS (2006)

20.

Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

21.

Zhou, Z., Wagner, A., Mobahi, H., Wright, J., Ma, Y.: Face recognition with contiguous occlusion using Markov random fields. In: IEEE International Conference Computer Vision (ICCV), pp. 1050–1057, October 2009

22.

He, R., Zheng, W.S., Tan, T., Sun, Z.: Half-quadratic-based iterative minimization for robust sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 36(2), 261–275 (2014)CrossRef

23.

Peng, Y., Li, L., Liu, S., Li, J., Cao, H.: Virtual samples and sparse representation based classification algorithm for face recognition. IET Comput. Vis. 13(2), 172–177 (2018)CrossRef

24.

Fritz, K., Damiana, L., Serena, M.: A robust group sparse representation variational method with applications to face recognition. IEEE Trans. Image Process. 28(6), 2785–2798 (2019)MathSciNetCrossRef

25.

Weng, R., Lu, J., Hu, J., Yang, G., Tan, Y.P.: Robust feature set matching for partial face recognition. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 601–608, December 2013

26.

Wei, X., Li, C.-T., Lei, Z., Yi, D., Li, S.Z.: Dynamic image-to-class warping for occluded face recognition. IEEE Trans. Inf. Forensics Secur. 9(12), 2035–2050 (2014)

27.

Chen, W., Gao, Y.: Face Recognition Using Ensemble String Matching. IEEE Trans. Image Process. 22(12), 4798–4808 (2013)MathSciNetCrossRef

28.

Gao, Y., Leung, M.K.H.: Human face profile recognition using attributed string. Pattern Recognit. 35(2), 353–360 (2002)

29.

Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2013)CrossRef

30.

Mehdipour Ghazi, M., Kemal Ekenel, H.: A comprehensive analysis of deep learning-based representation for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 34–41 (2016)

31.

Lu, J., Wang, G., Zhou, J.: Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans. Image Process. 26, 4042–4054 (2017)MathSciNetCrossRef

32.

Hu, G, Peng, X.Y., Hospedales, Y., Verbeek, T.M., Frankenstein, J.: Learning deep face representations using small data. IEEE Trans. Image Process. 27, 293–303 (2018)

33.

Rawat, W., Wang, Z.: Deep convolutional neural networks for image classification: a comprehensive review. Neural Comput. 29, 2352–2449 (2017)MathSciNetCrossRef

34.

Krishnaveni, B., Sridhar, S.: Approximation algorithm based on greedy approach for face recognition with partial occlusion. Multimed. Tools Appl. 78, 27511–27531 (2019)CrossRef

35.

Tenorio, E.Z., Thomaz, C.E.: Analisemultilinear discriminate deformas frontalis de imagens 2D de face. In: Proceedings of the X Simposio Brasileiro de Automacao Inteligente, SBAI, Universidade Federal de Sao Joao del Rei, Sao Joao del Rei, Minas Gerais, Brazil, pp. 266–271, September 2011

36.

https://fei.edu.br/~cet/facedatabase.html

37.

The Database of Faces, AT&T Laboratories Cambridge (2002). http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase.html

Titel: Role of Distance Measures in Approximate String Matching Algorithms for Face Recognition System
verfasst von: B. Krishnaveni
S. Sridhar
Verlag: Springer International Publishing
Buch: Computational Intelligence in Data Science
Print ISBN: 978-3-030-63466-7

Electronic ISBN: 978-3-030-63467-4

Copyright-Jahr: 2020
DOI: https://doi.org/10.1007/978-3-030-63467-4_12

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"