Call for Paper - January 2022 Edition
IJCA solicits original research papers for the January 2022 Edition. Last date of manuscript submission is December 20, 2021. Read More

Face Recognition using DCT based Energy Discriminant Mask

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Authors:
Vikas Maheshkar, Sushila Maheshkar
10.5120/ijca2017914846

Vikas Maheshkar and Sushila Maheshkar. Face Recognition using DCT based Energy Discriminant Mask. International Journal of Computer Applications 170(5):46-51, July 2017. BibTeX

@article{10.5120/ijca2017914846,
	author = {Vikas Maheshkar and Sushila Maheshkar},
	title = {Face Recognition using DCT based Energy Discriminant Mask},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {5},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {46-51},
	numpages = {6},
	url = {http://www.ijcaonline.org/archives/volume170/number5/28070-2017914846},
	doi = {10.5120/ijca2017914846},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

It has been observed that the variations among the images of the same face due to illumination and viewing direction are almost always larger than image variations. One person, with the same facial expression, can appear strikingly different when light source direction and viewpoint vary. These variations are emphasized by additional factors such as facial expressions, perspiration, hair style, cosmetics, and even changes due to aging. The proposed Face recognition technique is based on Energy discriminant mask obtained by thresholding DCT coefficients in low, mid and high frequency regions. The proposed approach analyzes all images of a database to know the discrimination ability of individual DCT coefficient and generates a database specific DCT mask. High recognition rate can be achieved by using the coefficients that have maximum discrimination power. To benchmark proposed techniques standard ORL and YALE face databases are used.

References

  1. Belhumeur, P., Hespanha, J., and Kriegman, D. Eigenfaces Vs. Fisherfaces: Recognition using class specific linear projection. ieee transactions on pattern analysis and machine intelligence 19, 7 (1997), 711–720.
  2. Choi, J., Chung, Y.-S., Kim, K.-H., and Yoo, J.-H. Face recognition using energy probability in DCT domain. in 2006 ieee international conference on multimedia and expo (2006), pp. 1549–1552.
  3. Dabbaghchian, S., Ghaemmaghami, M. P., and Aghagolzadeh, A. Feature extraction using discrete cosine transform and discrimination power analysis with a face recognition technology. pattern recognition 43, 4 (2010), 1431–1440
  4. Georghiades, A. Yale face database. center for computational vision and control at yale university. http://cvc. yale. edu/projects/yalefaces/yalefaces (1997).
  5. Georghiades, A., Belhumeur, P., and Kriegman, D. Illumination cone models for face recognition under variable lighting and pose. IEEE trans. pattern anal. mach. intelligence 23, 6 (2001), 643–660.
  6. Gross, R., and Brajovic, V. An image preprocessing algorithm for illumination invariant face recognition. 4th international conference on audio- and video-based biometric person authentication (berlin, heidelberg, 2003), avbpa’03, springer-verlag, pp. 10–18.
  7. Jia, X., and Nixon, M. S. Extending the feature vector for automatic face recognition. IEEE transactions on pattern analysis and machine intelligence 17, 12 (1995), 1167–1176.
  8. Kaya, Y., and Kobayaski, K. A basic study on human face recognition. frontiers of pattern recognition (1972), 265–290.
  9. Mayer, C., Wimmer, M., and Radig, B. Adjusted pixel features for robust facial component classification. Image Vision Comput. 28, 5 (may 2010), 762–771.
  10. Norton, E. Identifying the brain’s own facial recognition system. 2012
  11. Sakai, T., Nagao, M., Kanade, T., and University, K. Computer analysis and classification of photographs of human faces. Kyoto University, 1972.
  12. Samal, A., and Iyengar, P. A. Automatic recognition and analysis of human faces and facial expressions: A survey. pattern recogn. 25, 1(jan 1992), 65–77.
  13. Samaria, F. S., and Harter, A. C. Parameterisation of a stochastic model for human face identification. in proceedings of the second IEEE workshop on applications of computer vision, 1994. (1994), IEEE, pp. 138–142.
  14. Štruc, V., and Paveši´C , N. Performance evaluation of photometric normalization techniques for illumination invariant face recognition. advances in face image analysis: techniques and technologies (2010).
  15. Swarup Ku. and Dandpat, S. M. New technique for DCT-PCA based face recognition. In international conference on electronic systems, national institute of technology, Rourkela, India (2011).
  16. Turk, M., and Pentland, A. Face recognition using eigenfaces. in IEEE computer society conference on computer vision and pattern recognition, 1991. proceedings CVPR ’91. (1991), pp. 586–591.
  17. Wölfel, M., And Ekenel, H. K. Feature weighted mahalanobis dis- tance: improved robustness for gaussian classifiers. In 13th European Signal Processing Conference (2005). 

  18. Yin, H., Fu, P., and Qiao, J. Face recognition based on dct and 2dlda. in second international conference on innovative computing, information and control, 2007. ICICIC ’07. (2007), pp. 581–581.
  19. Zhao, W., Chellappa, R., Phillips, P. J., and Rosenfeld, A. Face recognition: a literature survey. Acm Comput. Surv. 35, 4 (dec 2003), 399–458.
  20. Zhou, D., Yang, X., Peng, N., and Wang, Y. Improved-LDA based face recognition using both facial global and local information. Pattern Recogn. Lett. 27, 6 (Apr 2006), 536–543

Keywords

Biometrics, Face recognition, Discriminant, DCT, Mask, Energy.