Skip to main content
Log in

Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition

Journal of Computer Science and Technology Aims and scope Submit manuscript

Abstract

An innovative and uniform framework based on a combination of Gabor wavelets with principal component analysis (PCA) and multiple discriminant analysis (MDA) is presented in this paper. In this framework, features are extracted from the optimal random image components using greedy approach. These feature vectors are then projected to subspaces for dimensionality reduction which is used for solving linear problems. The design of Gabor filters, PCA and MDA are crucial processes used for facial feature extraction. The FERET, ORL and YALE face databases are used to generate the results. Experiments show that optimal random image component selection (ORICS) plus MDA outperforms ORICS and subspace projection approach such as ORICS plus PCA. Our method achieves 96.25%, 99.44% and 100% recognition accuracy on the FERET, ORL and YALE databases for 30% training respectively. This is a considerably improved performance compared with other standard methodologies described in the literature.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Zhao W, Chellappa R, Phillips PJ, Rosenfeld A (2003) Face recognition: A literature survey. ACM Computing Surveys 35(4):399–458

    Article  Google Scholar 

  2. Gottumukkal R, Asari V (2004) An improved face recognition technique based on modular PCA approach. Pattern Recognition Letters 25(4):429–436

    Article  Google Scholar 

  3. Zou J, Ji Q, Nagy G (2007) A comparative study of local matching approach for face recognition. IEEE Transactions on Image Processing 16(10):2617–2628

    Article  MathSciNet  Google Scholar 

  4. Retna Swami MSSK, Karuppiah M (2011) An improved face recognition technique based on modular LPCA approach. Journal of Computer Science 7(12):1900–1907

    Article  Google Scholar 

  5. Pentland A, Moghaddam B, Starner T. View-based and modular eigenspaces for face recognition. In Proc. IEEE Conf. Computer Vision and Pattern Recognition, June 1994, pp.84–91.

  6. Heisele B, Ho P, Wu J, Poggio T (2003) Face recognition: Component-based versus global approaches. Computer Vision and Image Understanding 91(1):6–21

    Article  Google Scholar 

  7. Fang Y, Tan T, Wang Y. Fusion of global and local features for face verification. In Proc. the 16th IEEE Int. Conf. Pattern Recognition, August 2002, Vol.2, pp.382–385.

  8. Lei Z, Liao S, Pietikäinen M, Li S (2011) Face recognition by exploring information jointly in space, scale and orientation. IEEE Transactions on Image Processing 20(1):247–256

    Article  MathSciNet  Google Scholar 

  9. Liu C, Wechsler H (2002) Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition. IEEE Trans Image Processing 11(4):467–476

    Article  Google Scholar 

  10. Su Y, Shan S, Chen X, Gao W (2009) Hierarchical ensemble of global and local classifiers for face recognition. IEEE Transactions on Image Processing 18(8):1885–1896

    Article  MathSciNet  Google Scholar 

  11. Turk M, Pentland A (1991) Eigenfaces for recognition. Journal of Cognitive Neuroscience 3(1):71–86

    Article  Google Scholar 

  12. Turk M, Pentland A. Face recognition using eigenfaces. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 1991, pp.586–591.

  13. Sirovitch L, Kirby M (1987) Low-dimensional procedure for the characterization of human faces. Journal of the Optical Society of America 4(3):519–524

    Article  Google Scholar 

  14. Xiang C, Fan X, Lee T (2006) Face recognition using recursive fisher linear discriminant. IEEE Transactions on Image Processing 15(8):2097–2105

    Article  Google Scholar 

  15. Belhumeur P, Hespanha J, Kriegman D (1997) Eigenfaces vs. fisher-faces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7):711–720

    Article  Google Scholar 

  16. Shen L, Bai L, Fairhurst M (2007) Gabor wavelets and general discriminant analysis for face identification and verification. Image Vision and Computing 25(5):553–563

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Mathu Soothana S. Kumar Retna Swami.

Electronic Supplementary Material

Below is the link to the electronic supplementary material.

(DOC 29.6 KB)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Swami, M.S.S.K.R., Karuppiah, M. Optimal Feature Extraction Using Greedy Approach for Random Image Components and Subspace Approach in Face Recognition. J. Comput. Sci. Technol. 28, 322–328 (2013). https://doi.org/10.1007/s11390-013-1333-5

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11390-013-1333-5

Keywords

Navigation