International E-publication: Publish Projects, Dissertation, Theses, Books, Souvenir, Conference Proceeding with ISBN.  International E-Bulletin: Information/News regarding: Academics and Research

Performance Comparison of Face Recognition Algorithms based on face image Retrieval

Author Affiliations

  • 1Laboratory of Solid Mechanics and Systems, Faculty of science and engineering, University of M’hamed Bougara Boumerdés, ALGERIA
  • 2 Laboratory of Energetic, Mechanics and Engineering, Faculty of science and engineering, University of M’hamed Bougara Boumerdés, ALGERIA

Res. J. Recent Sci., Volume 2, Issue (12), Pages 65-73, December,2 (2013)


Biometric systems are complex systems of safety measures based on physical, biological and the humans behavioral. The automatic face recognition has become a highly active research area, mainly due to numbers published papers in recent years. In this paper, we present a comparative study for evaluation of face recognition system based on face restoration. Our study is performed in two consecutive steps, In the first step, we use two methods of image restoration called Centralized sparse representation (CSR) and adaptive sparse domain selection with adaptive regularization (ASDS-AR) while in the second step the set of methods that have been used are principal component analysis (PCA), linear discriminant analysis (LDA) kernel principal component analysis (KPCA) and Kernel Fisher Analysis (KFA) for face recognition and we associated the Gabor Wavelets and Phase Congruency in order to achieve the evaluation of our proposed model. In addition, the comparative analysis on the ORL database is also employed in the experiments to evaluate the susceptibility of the appearance based methods on various image degradations which can occur in ”real-life” operating conditions. Our experimental results suggest that Gabor linear discriminant analysis (GLDA) ensures the most consistent verification rates across the tested ORL databases for both methods CSR and ASDS-AR.


  1. Li S.Z. and Jain. A.K., editors. Handbook of Face Recognition. SpringerVerlag, New York, (2005)
  2. Nishiyama M,. Takeshima H, Shotton J., Kozakaya. T., and Yamaguchi. O, Facial deblur inference to improve recognition of blurred faces. Proc. CVPR, 1115– 1122(2009)
  3. Ramponi. G and cubic. A, unsharp masking technique for contrast enhancement, Signal Processing, 67(2), 211–222 (1998)
  4. Ramponi. G and Polesel. A, Rational unsharp masking technique. Journal of Electronic Imaging, 7(2), 333–338(1998)
  5. Chan. T.G and Wong C. K.. Total variation blind deconvolution. IEEE Trans. on Image Processing, 7(3), 370–375 (1998)
  6. Yao. Y, Abidi. B and Abidi. M, Quality Assessment and Restoration of Face Images in Long Range/High Zoom Video (Chap 4), Springer, Berlin,43–60 (2007)
  7. Struc. V, Pavesic. N, Gabor-based kernel partial-least-squares discrimination features for face recognition, Informatica, 2 (20), 115–138 (2009)
  8. Struc. V, Pavesic. N., The complete Gabor-Fisher classifierfor robust face recognition, EURASIP. Journal of Adv Signal Process, article ID 847680,(2010)
  9. Batagelj B. and Solina F., Face recognition in different subspaces – a comparative study, in Proc. of the 6th International Workshop on Pattern Recognition in Information Systems, PRIS’06, 71–80 (2006)
  10. Esbati H. and Shirazi J., face recognition with PCA and KPCA using Elman neural network and SVM, world academy of science engineering and technology, 5 (52), 174-178 (2011)
  11. Baochang Z., Gabor-kernel fisher analysis for face recognition, In Proc of PCM (2), 802–809 (2004)
  12. Chan C., Kittler J. and Messer K., Multi-scale local binary pattern histograms for face recognition, in Proc. Int. Conf. Biometrics, 809–818 (2007)
  13. Shen. L, Bai. L. Bardsley. D and Yangsheng. W Gabor feature selection for face recognition using improved adaboost learning. In Proc of IWBRS, 39–49 (2005)
  14. Y. Su, Shiguang. S, Xilin C, and Wen. G. Multiple fisher classifiers combination for face recognition based on grouping adaboosted gabor features, In Proc of BMVC,(2006)
  15. Zahra M. Image Duplication Forgery Detection using Two Robust Features. Res J. of Recent Sci. 1(12), 1-6 (2012)
  16. Laurenz W, Fellous. J. M, Kruger. N, and Malsburg. C. D. Face recognition by elastic bunch graph matching. IEEE Trans. On Pattern Analysis and Machine Intelligence, 19(7), 775–779 (1997)
  17. Zhang B., Shan S., Chen X. and Gao W., Histogram of Gabor phase patterns: A novel objects representation approach for face recognition, Image Processing, IEEE Transactions on, 16(1), 57–68 (2007)
  18. Hong L., Jain A, Pankanti S and Bolle R., Fingerprint enhancement. In Proceedings of the 1st IEEE WACV, Sarasota, 202–207 (1996)
  19. Wiskott L., Fellous J.M., Kruger N., Malsburg C.V., Face recognition by elastic bunch graph matching, IEEE Trans on Pattern Analysis & Machine Intel, 8(19), 775–779(1997)
  20. Kong W.K., Zhang D. and Li W., Palmprint feature extraction using 2-D Gabor filters, Patt Recog, 36(10), 2339–2347 (2003)
  21. Kovesi P., Image features from phase congruency, Videre: J. of Computer Vision Res, 1(3), 1–26 (1999)
  22. Venkatesh. S and Owens. R., An energy feature detection scheme, in Proceedings of the International Conf onImage Proces. 553–557, Singapore, (1989)
  23. Patheja P.S., Akhilesh.W A. and Maurya J. P. An Enhanced Approch for Content Based Image Retrieval,Features, Res J. of Recent sci.,1(ISC-2011) , 415-418(2012)
  24. Dong W., Zhang L. and Shi G., Centralized sparse representation for image restoration, in Proc of the IEEE (ICCV), Barcelona (2011)
  25. Elad M., Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing, in: Springer (Eds.), Iterative-Shrinkage Algorithms, 111-138 (2010)
  26. Dong. W, Zhang. L, Shi. G. and. Wu. X, Image deblurring and super-resolution by adaptive sparse domain selection and adaptive regularization, IEEE Trans Image Process, 20(7), 1838–1857 (2011)