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Face Authentication using Euclidean Distance Model with PSO Algorithm

IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)
© 2015 by IJCA Journal
ICICT 2015 - Number 3
Year of Publication: 2015
R. Senthilkumar
M. Vigneshwaran
S. Uma

R.senthilkumar, M.vigneshwaran and S.uma. Article: Face Authentication using Euclidean Distance Model with PSO Algorithm. IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015) ICICT 2015(3):16-19, July 2015. Full text available. BibTeX

	author = {R.senthilkumar and M.vigneshwaran and S.uma},
	title = {Article: Face Authentication using Euclidean Distance Model with PSO Algorithm},
	journal = {IJCA Proceedings on International Conference on Innovations in Computing Techniques (ICICT 2015)},
	year = {2015},
	volume = {ICICT 2015},
	number = {3},
	pages = {16-19},
	month = {July},
	note = {Full text available}


In recent technological world lot of devices are invented. Moreover the focused topic is on security system. Even with lot of security system like finger print based, eye-retina based, pin-code based systems are available, face recognition based security system has vital role of advanced technology. Feature based Face authentication requires feature extraction, feature selection and classification. Face recognition process performance is mainly depended on the selection of such extractor and classifier. Feature extraction process gives the feature points of the face in the image. From that important fiducial points are extracted using feature selection process. One have to reduce the feature points, in order to obtain the fast response of recognition. In this work we proposed the feature extraction process with Gabor filter where it is convenient as a biometric filter. Before going to verification process face localization is important one, then only we can reduce the unnecessary feature points. This is done by Neural network classifier. After the face image is obtained, we go for the authentication process with modified Euclidean distance of each fiducial points that made coefficient model for each person. Best optimized Euclidean distance coefficient of images are obtrained through PSO algorithm. Thus the coefficient of test and trained images are given to the classifier and the minimum mean difference profile made as the matching profile. By this work, we reduced the perception time at significant level compared to previous work and we made the recognition rate of the work as 91. 81 percent with PIE database.


  • Shape-driven Gabor jets for face description and authentication,Daniel González-Jiménez And José Luis Alba-Castro, IEEE transactions on information forensics and security, vol. 2, no. 4, December 2007.
  • Detecting faces in images: a survey, Ming-Hsuan Yang, member, IEEE, David J. Kriegman, senior member, IEEE, and Narendra Ahuja, fellow, IEEE . IEEE transactions on pattern analysis and machine intelligence, vol. 24, no. 1, January 2002.
  • Face localization and authentication using color and depth images, Filareti Tsalakanidou, Sotiris Malassiotis, And Michael G. Strintzis, fellow, IEEE. IEEE transactions on image processing, vol. 14, no. 2, February 2005.
  • Learning from examples in the small sample case: face expression recognition Guodong Guo And Charles R. Dyer, fellow, IEEE. IEEE transactions on systems, man, and cybernetics—part b: cybernetics, vol. 35, no. 3, June 2005 m.
  • S. Arulampalam, S. Maskell, N. Gordon, And T. Clapp, "A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking," IEEE trans. Signal process. , vol. 50, no. 2, pp. 174–188, Feb. 2002.
  • A. Jain, R. Bolle, and S. Pankanti, Biometrics: Personal Identification in Networked Society, Kluwer Academic Publishers, 1999.
  • D. Zhang, Biometric Solutions For Authentication In An E-World, Kluwer Academic Publishers, 2002.
  • K. Lee, H. Byun, "A new face authentication system for memory constrained devices," IEEE Transactions on Consumer Electronics, vol. 49, no. 4, pp. 1214-1222, Nov. 2003.
  • B. Toth, "Biometric liveness detection," Information Security Bulletin,vol. 10, pp. 291–297, 2005.
  • Lin Sun, Gang Pan, Zhaohui Wu, "Blinking-based live face detection using conditional random fields," LNCS 4642, pp. 252-260, 2007.
  • J. Li, Y. Wang, T. Tan, and A. K. Jain, "Live face detection based on the analysis of fourier spectra," SPIE vol. 5404, pp. 296-303, 2004.
  • P. J. Phillips, H. Moon, S. A. Rizvi, and P. J. Rauss, "The FERET evaluation methodology for face recognition algorithms," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 22, pp. 1090–1104, 2000.
  • P. Viola and M. J. Jones, "Robust real-time object detection," Int. J. Comput. Vis. , vol. 57, no. 2, pp. 137–154, 2004.
  • Y. Freund and R. Schapire, "A decision theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci. ,vol. 55, pp. 119–139, 1997.
  • R. Lienhart, A . Kuranov, and V. Pisarevsky, "Empirical analysis ofdetection cascades of boosted classifiers for rapid object," in DAGM25th Pattern Recognition Symp. , 2003, pp. 297–304.
  • H. A. Rowley, S. Baluja, and T. Kanade, "Neural network-based face detection," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 20, no. 1, pp. 23–38, 1998
  • P. J. Phillips, H. Wechsler, J. Huang, and P. Rauss, "The FERET database and evaluation procedure for face recognition algorithms," J. Image Vis. Comput. , vol. 16, no. 5, pp. 295–306, 1998.