Skip to main content
SpringerLink
Log in
Book cover

Hybrid Metaheuristics for Image Analysis pp 49–86Cite as

Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers

  • Chapter
  • First Online:

Abstract

Hybridization represents a promising approach for solving any recognition problem. This chapter presents two face recognition frameworks involving the hybridization of both the feature extraction and classification stages. Feature extraction is performed through the two proposed hybrid techniques, one based on the orthogonal combination of local binary patterns and histogram of oriented gradients, and the other based on gabor filters and Zernike moments. A hybrid metaheuristic classifier is also investigated for classification based on the integration of genetic algorithms (GA) and support vector machines (SVM), where GA has been used for the optimization of the SVM parameters. This is crucial since the optimal selection of SVM parameters ultimately governs its recognition accuracy. Experimental results and comparisons prove the suitability of the proposed frameworks as compared to the other baseline and previous works.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. T. Ahonen, A. Hadid, M. Pietikäinen, Face recognition with local binary patterns, in European Conference on Computer Vision (Springer, Berlin, 2004), pp. 469–481

    Google Scholar 

  2. A. Albiol, D. Monzo, A. Martin, J. Sastre, A. Albiol, Face recognition using hog–ebgm. Pattern Recogn. Lett. 29(10), 1537–1543 (2008)

    Google Scholar 

  3. A. Amine, M. Rziza, D. Aboutajdine, Svm-based face recognition using genetic search for frequency-feature subset selection, in Image and Signal Processing (Springer, Berlin, 2008), pp. 321–328

    Google Scholar 

  4. P.N. Belhumeur, J.P. Hespanha, D.J. Kriegman, Eigenfaces vs. fisherfaces: recognition using class specific linear projection. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 711–720 (1997)

    Google Scholar 

  5. A.A. Bhuiyan, C.H. Liu, On face recognition using gabor filters. World Acad. Sci. Eng. Technol. 28, 51–56 (2007)

    Google Scholar 

  6. C.H. Chan, J. Kittler, K. Messer, Multi-scale local binary pattern histograms for face recognition, in Advances in Biometrics (2007), pp. 809–818

    Google Scholar 

  7. C.C. Chang, C.J. Lin, Libsvm: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)

    Google Scholar 

  8. J.S. Chou, M.Y. Cheng, Y.W. Wu, A.D. Pham, Optimizing parameters of support vector machine using fast messy genetic algorithm for dispute classification. Expert Syst. Appl. 41(8), 3955–3964 (2014)

    Google Scholar 

  9. I.J. Cox, J. Ghosn, P.N. Yianilos, Feature-based face recognition using mixture-distance, in 1996 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1996. Proceedings CVPR’96 (IEEE, Washington, 1996), pp. 209–216

    Google Scholar 

  10. N. Dalal, B. Triggs, Histograms of oriented gradients for human detection, in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005. CVPR 2005, vol. 1 (IEEE, Washington, 2005), pp. 886–893

    Google Scholar 

  11. O. Déniz, M. Castrillon, M. Hernández, Face recognition using independent component analysis and support vector machines. Pattern Recogn. Lett. 24(13), 2153–2157 (2003)

    Google Scholar 

  12. O. Déniz, G. Bueno, J. Salido, F. De la Torre, Face recognition using histograms of oriented gradients. Pattern Recogn. Lett. 32(12), 1598–1603 (2011)

    Google Scholar 

  13. M. El Aroussi, M. El Hassouni, S. Ghouzali, M. Rziza, D. Aboutajdine, Local appearance based face recognition method using block based steerable pyramid transform. Signal Process. 91(1), 38–50 (2011)

    Google Scholar 

  14. N.H. Foon, Y.H. Pang, A.T.B. Jin, D.N.C. Ling, An efficient method for human face recognition using wavelet transform and Zernike moments, in Proceedings. International Conference on Computer Graphics, Imaging and Visualization, 2004. CGIV 2004 (IEEE, Washington, 2004), pp. 65–69

    Google Scholar 

  15. J.B. Gao, Q.H. Cao, Adaptive hog-lbp based learning for palm tracking. Adv. Mater. Res. 756, 3707–3711 (2013)

    Google Scholar 

  16. M. Ghorbani, A.T. Targhi, M.M. Dehshibi, Hog and lbp: towards a robust face recognition system, in 2015 Tenth International Conference on Digital Information Management (ICDIM) (IEEE, Washington, 2015), pp. 138–141

    Google Scholar 

  17. Z.M. Hafed, M.D. Levine, Face recognition using the discrete cosine transform. Int. J. Comput. Vis. 43(3), 167–188 (2001)

    Google Scholar 

  18. M. Heikkilä, M. Pietikäinen, C. Schmid, Description of interest regions with local binary patterns. Pattern Recogn. 42(3), 425–436 (2009)

    Google Scholar 

  19. G. Huang, Fusion (2d) 2 pcalda: a new method for face recognition. Appl. Math. Comput. 216(11), 3195–3199 (2010)

    Google Scholar 

  20. D. Huang, C. Shan, M. Ardabilian, Y. Wang, L. Chen, Local binary patterns and its application to facial image analysis: a survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(6), 765–781 (2011)

    Google Scholar 

  21. P. Huang, C. Chen, Z. Tang, Z. Yang, Feature extraction using local structure preserving discriminant analysis. Neurocomputing 140, 104–113 (2014)

    Google Scholar 

  22. Z.H. Huang, W.J. Li, J. Wang, T. Zhang, Face recognition based on pixel-level and feature-level fusion of the top-level’s wavelet sub-bands. Inform. Fusion 22, 95–104 (2015)

    Google Scholar 

  23. M. Lades, J.C. Vorbruggen, J. Buhmann, J. Lange, C. von der Malsburg, R.P. Wurtz, W. Konen, Distortion invariant object recognition in the dynamic link architecture. IEEE Trans. Comput. 42(3), 300–311 (1993)

    Google Scholar 

  24. M. Li, X. Zhou, X. Wang, B. Wu, Genetic algorithm optimized svm in object-based classification of quickbird imagery, in 2011 IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) (IEEE, Washington, 2011), pp. 348–352

    Google Scholar 

  25. Z. Liu, C. Liu, Fusion of color, local spatial and global frequency information for face recognition. Pattern Recogn. 43(8), 2882–2890 (2010)

    Google Scholar 

  26. G.H. Liu, J.Y. Yang, Content-based image retrieval using color difference histogram. Pattern Recogn. 46(1), 188–198 (2013)

    Google Scholar 

  27. G.F. Lu, Z. Jin, J. Zou, Face recognition using discriminant sparsity neighborhood preserving embedding. Knowl.-Based Syst. 31, 119–127 (2012)

    Google Scholar 

  28. Y. Peng, S. Wang, X. Long, B.L. Lu, Discriminative graph regularized extreme learning machine and its application to face recognition. Neurocomputing 149, 340–353 (2015)

    Google Scholar 

  29. H. Ren, H. Ji, Nonparametric subspace analysis fused to 2dpca for face recognition. Optik—Int. J. Light Electron. Opt. 125(8), 1922–1925 (2014)

    Google Scholar 

  30. C. Singh, A.M. Sahan, Face recognition using complex wavelet moments. Opt. Laser Technol. 47, 256–267 (2013)

    Google Scholar 

  31. C. Singh, N. Mittal, E. Walia, Face recognition using Zernike and complex Zernike moment features. Pattern Recognit. Image Anal. 21(1), 71–81 (2011)

    Google Scholar 

  32. C. Singh, E. Walia, N. Mittal, Robust two-stage face recognition approach using global and local features. Vis. Comput. 28(11), 1085–1098 (2012)

    Google Scholar 

  33. C. Singh, N. Mittal, E. Walia, Complementary feature sets for optimal face recognition. EURASIP J. Image Video Process. 2014(1), 35 (2014)

    Google Scholar 

  34. K.R. Soundar, K. Murugesan, Preserving global and local information–a combined approach for recognising face images. IET Comput. Vis. 4(3), 173–182 (2010)

    Google Scholar 

  35. H. Spies, I. Ricketts, Face recognition in Fourier space, in Vision Interface, vol. 2000 (2000), pp. 38–44

    Google Scholar 

  36. V. Štruc, R. Gajšek, N. Pavešic, Principal gabor filters for face recognition, in Proceedings of the 3rd IEEE International Conference on Biometrics: Theory, Applications and Systems (IEEE Press, Piscataway, 2009), pp. 113–118

    Google Scholar 

  37. X. Tan, B. Triggs, Fusing gabor and lbp feature sets for kernel-based face recognition, in International Workshop on Analysis and Modeling of Faces and Gestures (Springer, Berlin, 2007), pp. 235–249

    Google Scholar 

  38. H. Tan, B. Yang, Z. Ma, Face recognition based on the fusion of global and local hog features of face images. IET Comput. Vis. 8(3), 224–234 (2013)

    Google Scholar 

  39. M. Turk, A. Pentland, Eigenfaces for recognition. J. Cogn. Neurosci. 3(1), 71–86 (1991)

    Google Scholar 

  40. Y. Wang, Y. Wu, Face recognition using intrinsicfaces. Pattern Recogn. 43(10), 3580–3590 (2010)

    Google Scholar 

  41. X. Wang, T.X. Han, S. Yan, An hog-lbp human detector with partial occlusion handling, in 12th International Conference on Computer Vision, 2009 IEEE (IEEE, Washington, 2009), pp. 32–39

    Google Scholar 

  42. L. Wiskott, N. Krüger, N. Kuiger, C. Von Der Malsburg, Face recognition by elastic bunch graph matching. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 775–779 (1997)

    Google Scholar 

  43. S. Xie, S. Shan, X. Chen, X. Meng, W. Gao, Learned local gabor patterns for face representation and recognition. Signal Process. 89(12), 2333–2344 (2009)

    Google Scholar 

  44. A.L. Yuille, P.W. Hallinan, D.S. Cohen, Feature extraction from faces using deformable templates. Int. J. Comput. Vis. 8(2), 99–111 (1992)

    Google Scholar 

  45. G. Zhang, X. Huang, S.Z. Li, Y. Wang, X. Wu, Boosting local binary pattern (lbp)-based face recognition, in Sinobiometrics (Springer, Berlin, 2004), pp. 179–186

    Google Scholar 

  46. W. Zhang, S. Shan, W. Gao, X. Chen, H. Zhang, Local gabor binary pattern histogram sequence (lgbphs): a novel non-statistical model for face representation and recognition, in Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 1 (IEEE, Los Alamitos, 2005), pp. 786–791

    Google Scholar 

  47. B. Zhang, S. Shan, X. Chen, W. Gao, Histogram of gabor phase patterns (hgpp): a novel object representation approach for face recognition. IEEE Trans. Image Process. 16(1), 57–68 (2007)

    Google Scholar 

  48. W. Zhang, S. Shan, L. Qing, X. Chen, W. Gao, Are gabor phases really useless for face recognition? Pattern Anal. Appl. 12(3), 301–307 (2009)

    Google Scholar 

  49. J. Zhang, K. Huang, Y. Yu, T. Tan, Boosted local structured hog-lbp for object localization, in 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (IEEE, Washington, 2011), pp. 1393–1400

    Google Scholar 

  50. D. Zhang, J. Xiao, N. Zhou, M. Zheng, X. Luo, H. Jiang, K. Chen, A genetic algorithm based support vector machine model for blood-brain barrier penetration prediction. Biomed. Res. Int. 2015, 292683 (2015)

    Google Scholar 

  51. R. Zhi, Q. Ruan, Two-dimensional direct and weighted linear discriminant analysis for face recognition. Neurocomputing 71(16), 3607–3611 (2008)

    Article  Google Scholar 

  52. D. Zhou, X. Yang, Feature fusion based face recognition using efm, in International Conference Image Analysis and Recognition (Springer, Berlin, 2004), pp. 643–650

    Google Scholar 

  53. D. Zhou, X. Yang, N. Peng, Y. Wang, Improved-lda based face recognition using both facial global and local information. Pattern Recogn. Lett. 27(6), 536–543 (2006)

    Article  Google Scholar 

  54. S.R. Zhou, J.P. Yin, J.M. Zhang, Local binary pattern (lbp) and local phase quantization (lbq) based on gabor filter for face representation. Neurocomputing 116, 260–264 (2013)

    Article  Google Scholar 

  55. C. Zhu, C.E. Bichot, L. Chen, Image region description using orthogonal combination of local binary patterns enhanced with color information. Pattern Recogn. 46(7), 1949–1963 (2013)

    Article  Google Scholar 

Download references

Acknowledgements

The authors are grateful to the National Institute of Standards and Technology, AT&T Laboratories, and the Computer Vision Laboratory, Computer Science and Engineering Department, University of California, San Diego for providing the FERET, ORL, and Yale face databases respectively. During the course of this study, the first author was funded by an INSPIRE-Junior Research Fellowship (IF120810) from the Department of Science and Technology, Govt. of India.

Author information

Authors and Affiliations

  1. Department of Computer Science and Applications, MCM DAV College for Women, Chandigarh, India

    Geetika Singh

  2. Department of Computer Science and Applications, Panjab University, Chandigarh, India

    Indu Chhabra

Authors
  1. Geetika Singh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Indu Chhabra
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Applications, RCC Institute of Information Technology, Kolkata, West Bengal, India

    Siddhartha Bhattacharyya

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Singh, G., Chhabra, I. (2018). Genetic Algorithm Implementation to Optimize the Hybridization of Feature Extraction and Metaheuristic Classifiers. In: Bhattacharyya, S. (eds) Hybrid Metaheuristics for Image Analysis. Springer, Cham. https://doi.org/10.1007/978-3-319-77625-5_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-77625-5_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77624-8

  • Online ISBN: 978-3-319-77625-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation