Skip to main content

Advertisement

SpringerLink
Log in

A robust wavelet based decomposition of facial images to improve recognition accuracy in standard appearance based statistical face recognition methods

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

In traditional appearance based face recognition approaches, the facial images in standard face databases are used as it is for testing standard statistical face recognition algorithms. Due to the camera angle, position and lightening conditions, the facial image captured contains occlusion. Human pose, facial expressions and alignment of face with respect to camera axis also result in occluded image. Sometimes the background of the face also covered during the facial image capturing. We proposed an approach, called wavelet based facial image decomposition (WBFD) and in this approach the facial images are decomposed using two levels wavelet transform. In the faces obtained after two level wavelet decomposition, the occlusion and background images are suppressed. The face images from Yale database are used for our experiments. In order to test the performance of proposed WBFD approach, the face images obtained from WBFD approach, resized Yale face and Yale face from the original Yale face database are tested with five standard statistical appearance based face recognition algorithms such as eigen face, Fischer discriminant analysis, kernel principal component analysis, independent component analysis and 2D principal component analysis. The experimental results obtained show that, the proposed WBFD approach gives high recognition accuracy and require minimum recognition time in seconds for all the five face recognition methods.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Turk, M.A., Pentland, A.P.: Face recognition using eigen faces. In: Proceedings of the IEEE Conference Computer Vision and Pattern Recognition, pp. 586–591 (1991)

  2. Turk, M.A., Pentland, A.P.: Eigen faces for recognition. J. Cognit. Neurosci. 3(1), 71–96 (1991)

    Article  Google Scholar 

  3. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Proceedings of the European Conference Computer Vision (1996)

  4. Swets, S., Weng, J.: Using discriminant eigen features for image retrieval. IEEE Trans. Pattern Anal. Mach. Intell. 18(8), 831–836 (1996)

    Article  Google Scholar 

  5. Yang, M.H.: Kernel eigenfaces vs. kernel fisherfaces: face recognition using kernel methods. In: Proceedings of the International Conference Automatic Face Gesture Recognition, pp. 215–220 (2002)

  6. Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Trans. Neural Netw. 13(6), 1450–1464 (2002). https://doi.org/10.1109/TNN.2002.804287

    Article  Google Scholar 

  7. Yang, J., Zhang, D., Frangi, A.F., Yang, J.: Two-dimensional PCA: a new approach to face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Article  Google Scholar 

  8. Senthilkumar, R., Gnanamurthy, R.K.: A comparative study of 2-DPCA face recognition method with other statistically based face recognition methods. J. Inst. Eng. India Ser. B 97, 425 (2016). https://doi.org/10.1007/s40031-015-0212-6

    Article  Google Scholar 

  9. The ORL Database of Faces. Our Database of Face. http://www.cl.cam.ac.uk/research/dtg/attarchive/facedatabase/html (1994)

  10. Yale Face database. Computer Vision Laboratory in the Computer Science Engineering Department of UCSD. http://vision.ucsd.edu/content/ yale-face-database (1994)

  11. Color and gray FERET database. Image group, Information access division of National Institute of Standards and Technology (NIST). http://www.nist.gov/itl/iad/ig/colorferet.cfm

  12. Phillips, P.J., Moon, H., Rauss, P.J., Rizvi, S.: The FERET evaluation methodology for face recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell 22(10), 1090–1104 (2000)

    Article  Google Scholar 

  13. Senthilkumar, R., Gnanamurthy, R.K.: A detailed survey on 2D and 3D still face and face video databases part I. In: IEEE International Conference Communication and Signal Processing, pp. 1405–1409 (2014). https://doi.org/10.1109/ICCSP.2014.6950080

  14. Senthilkumar, R., Gnanamurthy, R.K.: A detailed survey on 2D and 3D still face and face video databases part II. In: IEEE International Conference Recent Advances in Engineering and Computational Sciences, pp. 1-6 (2014). https://doi.org/10.1109/RAECS.2014.6799620

  15. Senthilkumar, R., Gnanamurthy, R.K.: A new approach in face recognition: duplicating facial images based on correlation study. In the KMO’16 Proceedings of the \(11^{{\rm th}}\) International Conference Knowledge Management in Organizations Conference, ACM press. USA. (2016). https://doi.org/10.1145/2925995.2926032

  16. Sable, A.H., Talbar, S.N.: A novel illumination invariant face recognition method based on PCA and WPD using YCbCr color space. Procedia Comput. Sci. ICCC 92(2016), 181–187 (2016)

    Article  Google Scholar 

  17. Abuzneid, M., Mahmood, A.: Performance improvement for 2-D face recognition using mulit-classifiier and BPN. In: IEEE International Conference Long Island Systems, Applications and Technology Conference (2016). https://doi.org/10.1109/LISAT.2016.7494134

  18. Senthilkumar, R., Gnanamurthy, R.K.: Improvement and solution to the problems arise in the implementation of facial image recognition algorithms using open source software scilab. World Appl. Sci. J. 34(12), 1754–1761 (2016). https://doi.org/10.5829/idosi.wasj.2016.1754.1761. 2016 ISSN 1818-4952 IDOSI Publications

    Article  Google Scholar 

  19. Senthilkumar, R., Gnanamurthy, R.K.: Construction of own face and video face databases for face recognition and testing with standard face recognition methods. Electron. Telecommun. Eng. Div. Board (2016). https://doi.org/10.13140/RG.2.2.31678.48964

  20. Senthilkumar, R., Gnanamurthy, R.K.: Performance analysis of 1D and 2D statistical measures on standard facial image databases. Middle-East J. Sci. Res. 24(12), 3826–3832 (2016). https://doi.org/10.5829/idosi.mejsr.2016.3826.3832. 2016 ISSN 1990-9233 IDOSI Publications

    Article  Google Scholar 

  21. Senthilkumar, R., Gnanamurthy, R.K.: Facial image database mining and classification analysis using different distance metrics. Adv. Comput. Commun. Syst. (ICACCS) (2017). https://doi.org/10.1109/ICACCS.2017.8014619

  22. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. Pearson Education, London (2004)

    Google Scholar 

  23. Senthilkumar, R., Gnanamurthy, R.K.: HANFIS: a new fast and robust approach for face recognition and facial image classification. In: International Conference on Smart Innovations in Communications and Computational Sciences-2017 (ICSICCS-2017) 2017/6/24

  24. Senthilkumar, R., Gnanamurthy, R.K.: Performance improvement in classification rate of appearance based statistical face recognition methods using SVM classifier. In: 2017 International Conference on Advanced Computing and Communication Systems (ICACCS-2017), 06–07 Jan, Coimbatore, India (2017)

Download references

Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Institute of Road and Transport Technology, Erode, 638 316, India

    R. Senthilkumar

  2. Department of Electronics and Communication Engineering, P.P.G. Institute of Technology, Coimbatore, India

    R. K. Gnanamurthy

Authors
  1. R. Senthilkumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. K. Gnanamurthy
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to R. K. Gnanamurthy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Senthilkumar, R., Gnanamurthy, R.K. A robust wavelet based decomposition of facial images to improve recognition accuracy in standard appearance based statistical face recognition methods. Cluster Comput 22 (Suppl 5), 12785–12794 (2019). https://doi.org/10.1007/s10586-018-1759-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-1759-1

Keywords

Search

Navigation