Cover
Vol. 13 No. 1 (2017)

Published: July 31, 2017

Pages: 104-113

Original Article

Face Recognition Approach Based on the Integration of Image Preprocessing, CMLABP and PCA Methods

Yaqeen S. Mezaal 1
1Medical Instrumentation Engineering Department Al-Esraa University College Baghdad, Iraq
History
  • Received: May 31, 2017
  • Available Online: July 31, 2017
DOI

https://doi.org/10.37917/ijeee.13.1.12

Abstract

Face recognition technique is an automatic approach for recognizing a person from digital images using mathematical interpolation as matrices for these images. It can be adopted to realize facial appearance in the situations of different poses, facial expressions, ageing and other changes. This paper presents efficient face recognition model based on the integration of image preprocessing, Co-occurrence Matrix of Local Average Binary Pattern (CMLABP) and Principle Component Analysis (PCA) methods respectively. The proposed model can be used to compare the input image with existing database images in order to display or record the citizen information such as name, surname, birth date, etc. The recognition rate of the model is better than 99%. Accordingly, the proposed face recognition system is functional for criminal investigations. Furthermore, it has been compared with other reported works in the literature using diverse databases and training images. .

Keywords

References

  1. W. Y. Zhao, R. Chellappa, P. J. Phillips, and A. Rosenfeld, “Face Recognition: A Literature Survey,” ACM Computing Surveys, vol. 34, no. 4, pp. 399–485, 2003.
  2. A. K. Jain, R. Ross, and S. Prabhakar, “An Introduction to Biometric Recognition,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 84–92, 2004.
  3. K. Dharavath, F. A. Talukdar, and R. H. Laskar, “Improving Face Recognition Rate with Image Preprocessing,” Indian Journal of Science and Technology, vol. 7, no. 8, pp. 1170–1175, 2014.
  4. J. Meng and Y. Yang, “Symmetrical Two Dimensional PCA with Image Measures in Face Recognition,” International Journal of Advanced Robotic Systems, vol. 9, pp. 238–248, 2012.
  5. C. Zhang and W.-S. Chen, “A Novel Fisher Criterion Based Approach for Face Recognition,” Proceedings of IEEE International Conference on Wavelet Analysis and Pattern Recognition, Tianjin, pp. 27–31, 2013.
  6. S. Nazari and M. S. Moin, “Face Recognition Using Global and Local Gabor Features,” Proceedings of 21st IEEE Iranian Conference on Electrical Engineering, Mashhad, pp. 1–4, 2013.
  7. A. Suruliandi, K. Meena, and R. Reena-Rose, “Local Binary Pattern and Its Derivatives for Face Recognition,” IET Computer Vision, vol. 6, no. 5, pp. 480–488, 2012.
  8. A. H. Bishak, Z. Ghandriz, and T. Taheri, “Face Recognition using Co-occurrence Matrix of Local Average Binary Pattern,” Journal of Selected Areas in Telecommunications, April Edition, pp. 15–19, 2012.
  9. J. Wright, A. Ganesh, A. Yang, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210–227, 2009.
  10. R. Min and J.-L. Dugelay, “Improved Combination of LBP and Sparse Representation Based Classification (SRC) for Face Recognition,” Proceedings of IEEE International Conference on Multimedia and Expo (ICME), Barcelona, pp. 1–6, 2011.
  11. P. S. Sonar and P. K. Ajmera, “Face Recognition using Local Texture Descriptor,” International Journal of Engineering Research and Technology, vol. 3, no. 4, pp. 642–646, 2014.
  12. Z.-Q. Zhao, D. S. Huang, and B.-Y. Sun, “Human Face Recognition Based on Multiple Features Using Neural Networks Committee,” Pattern Recognition Letters, vol. 25, no. 12, pp. 1351–1358, 2004.
  13. Y.-F. Hou, Z.-L. Sun, Y.-W. Chong, and C.-H. Zheng, “Low-Rank and Eigenface Based Sparse Representation for Face Recognition,” PLoS ONE, vol. 9, no. 10, pp. 1–14, 2014.
  14. E. Dong, Y. Fu, and J. Tong, “Face Recognition by PCA and Improved LBP Fusion Algorithm,” Applied Mechanics and Materials, vol. 734, pp. 562–567, 2015.
  15. Z. Mahmood, T. Ali, and S. U. Khan, “Effects of Pose and Image Resolution on Automatic Face Recognition,” IET Biometrics, vol. 5, no. 2, pp. 111–119, 2016.
  16. J. Rehab and F. Hardalac, “Retinal Blood Vessel Segmentation with Neural Network by Using Gray-Level Co-Occurrence Matrix-Based Features,” Journal of Medical Systems, vol. 38, no. 85, pp. 1–12, 2014.
  17. K. Nissim and E. Harel, “A Texture Based Approach to Defect Analysis of Grapefruits,” Georgia Institute of Technology, CS7321 Winter, 1997.
  18. R. Haralick, K. Shanmugam, and I. Dinstein, “Textural Features for Image Classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
  19. N. Otsu, “A Threshold Selection Method from Gray-Level Histogram,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979.
  20. R. C. Gonzalez and R. E. Woods, Digital Image Processing, Prentice Hall, New Jersey, 2008.
  21. R. Shams and R. A. Kennedy, “Efficient Histogram Algorithms for NVIDIA CUDA Compatible Devices,” Proceedings of International Conference on Signal Processing and Communications Systems, pp. 418–422, 2007.
  22. T. Ojala, M. Pietikainen, and D. Harwood, “A Comparative Study of Texture Measures with Classification Based on Feature Distributions,” Pattern Recognition, vol. 29, no. 1, pp. 51–59, 1996.
  23. T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
  24. M. Turk and A. Pentland, “Eigenfaces for Recognition,” Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.
  25. M. Slavkovic and D. Jevtic, “Face Recognition Using Eigenface Approach,” Serbian Journal of Electrical Engineering, vol. 9, no. 1, pp. 121–130, 2012.
  26. H. Zhou, Face Detection and Recognition, Biometrics From Fiction to Practice, Pan Stanford Publishing, Singapore, 2013.
  27. V. H. Gaidhane, Y. V. Hote, and V. Singh, “An Efficient Approach for Face Recognition Based on Common Eigenvalues,” Pattern Recognition, vol. 47, no. 5, pp. 1869–1879, 2014.