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Reseach Article

Complete Architecture of a Robust System of Face Recognition

by Abdellatif Hajraoui, Mohamed Sabri, Mohamed Fakir
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 1
Year of Publication: 2015
Authors: Abdellatif Hajraoui, Mohamed Sabri, Mohamed Fakir
10.5120/21666-4741

Abdellatif Hajraoui, Mohamed Sabri, Mohamed Fakir . Complete Architecture of a Robust System of Face Recognition. International Journal of Computer Applications. 122, 1 ( July 2015), 26-31. DOI=10.5120/21666-4741

@article{ 10.5120/21666-4741,
author = { Abdellatif Hajraoui, Mohamed Sabri, Mohamed Fakir },
title = { Complete Architecture of a Robust System of Face Recognition },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 1 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 26-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number1/21666-4741/ },
doi = { 10.5120/21666-4741 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:09:27.369971+05:30
%A Abdellatif Hajraoui
%A Mohamed Sabri
%A Mohamed Fakir
%T Complete Architecture of a Robust System of Face Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 1
%P 26-31
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Although human face recognition is a hard topic because of the multitude of parameters involved (e. g. variation in pose, illumination, facial expression, partial occlusions), it is very important to be interested and to invest in it viewed her many fields of application (identity authentication, physical and logical access control, video surveillance, human-machine interface. . . ). The work presented in this paper is in this context. Its objective is the implementation of a complete architecture of a robust face recognition system. In a first time, a new approach has been developed for the detection of faces in a 2D color image. Secondly, is focused on the feature extraction using an original approach which includes the Gabor descriptor and a pose estimator. Finally, to validate this research, the developed system is tested on standard databases: Caltech_Web, AT&T and Color FERET.

References
  1. X. Tana, C. Songcan, Z. Zhoub and F. Zhangb. Face recognition from a single image per person: A survey. Pattern recognition, Volume 39, Issue 9, pp. : 1725–1745, September 2006.
  2. R. Patel, N. Rathod and A. Shah. Comparative Analysis of Face Recognition Approaches: A Survey. International Journal of Computer Applications, Volume 57, No. 17, pp. : 0975 – 8887, November 2012.
  3. S. Asht and R. Dass. Pattern Recognition Techniques: A Review. International journal of Computer Science Telecommunications, Volume 3, Issue 8, pp. : 25-29, August 2012.
  4. R. Dass, R. Rani et D. Kumar. Face Recognition Techniques: A Review. International Journal of Engineering Research and Development. Volume 4, Issue 7, pp. :. 70-78, November 2012.
  5. V. Vijayakumari. Face Recognition Techniques: A Survey. World Journal of Computer Application and Technology, Volume 1, Issue 2, pp. : 41-50, 2013.
  6. P. J. Phillips 2, H. Wechsler, J. Huang and P. J. Rauss. The FERET database and evaluation procedure for face-recognition algorithms. Image Vision Computer. Volume 16, Issue 5, pp. : 295-306 (1998).
  7. D. M. Blackburn, J. M. Bone and P. J. Phillips. Face Recognition Vendor Test 2000. FRVT 2000 Evaluation Report, NIST. February 2001.
  8. W. Zhao, R. Chellappa, P. J. Phillips and A . Rosenfeld. Face recognition: a literature survey. ACM Computer Surv. Volume 35, Issue 4, pp: 399–459, 2003.
  9. S. J. D. Prince, J. H. Elder, J. Warrell and F. M. Felisberti: Tied Factor Analysis for Face Recognition across Large Pose Differences. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 30, pages: 970–984, 2008.
  10. X. Zhang and Y. Gao. Face recognition across pose: A review. Pattern Recognition, Volume 42, pp: 2876-2896, November 2009.
  11. A. Hajraoui and M. Sabri. Face Detection Algorithm based on Skin Detection, Watershed Method and Gabor Filters. International Journal of Computer Applications. Volume 94, No 6, May 2014.
  12. http://www. vision. caltech. edu/
  13. P. Viola and M. Jones 2. Robust real-time face detection. International Journal of Computer Vision, Volume 57, Issue 2, pp: 137–154, 2004.
  14. Y. BEN JEMAA and S. KHANFIR. Automatic local Gabor features extraction for face recognition. International Journal of Computer Science and Information Security, Volume 3, No. 1, 2009.
  15. L. Shen and L. Bai: A review on Gabor wavelets for face recognition. Pattern Analysis and Applications, Volume 9, Issue 2-3, pp: 273–292, 18 August 2006.
  16. Yi Jin and Qiu-Qi Ruan. Face Recognition Using Gabor-based improved supervised locality preserving projections. Computing and Informatics, Volume 28, pp: 81–95, 2009.
  17. A. HAJRAOUI, M. SABRI, O. BENCHAREF and M. FAKIR. A new approach for Face Recognition Based on PCA & Double LDA Treatment combined with SVM. IOSR Journal of Engineering. , Volume 2, Issue 4, pp: 685-691, April. 2012.
  18. E. M. Chutorian and M. M. Trivedi: Head Pose Estimation in Computer Vision: A Survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 31, pp: 607– 626, 2009.
  19. E. Osuna, R. Freund, and F. Girosi, "Training Support Vector Machines: An Application to Face Detection". In Proceedings of IEEE Conference Computer Vision and Pattern Recognition (CVPR'97), pp: 130-136, 17-19 June 1997, Puerto Rico.
  20. H-J. Lin, S-H. Yen, J-P. Yeh and M-J. Lin. Face Detection Based on Skin Color Segmentation and SVM Classification. In Second International Conference on Secure System Integration and Reliability Improvement (SSIRI '08), pp: 230-231, 14-17 July 2008, Yokohama.
  21. S. Kim, Y. J. Park, K. Toh and S. Lee. SVM-based feature extraction for face recognition. Pattern Recognition, Volume 43, Issue 8, pp. : 2871–2881, August 2010.
  22. M. O. Faruqe. Face recognition using PCA and SVM. Dans 3rd International Conference on Anti-counterfeiting, Security, and Identification in Communication, 2009 (ASID 2009), pp. : 97 – 101. Hong Kong, 20-22 Aout 2009.
  23. M. FEUILLOY. Machine learning algorithms for prediction study of syncope in humans. PhD thesis, University of Angers, 2009.
  24. www. cl. cam. ac. uk/Research/DTG/attarchive/pub/data/att_faces. zip
  25. www. nist. gov/itl/iad/ig/colorferet. cfm
Index Terms

Computer Science
Information Sciences

Keywords

Face recognition face detection Gabor wavelets pose estimator Supports Vectors Machines.