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Reseach Article

Human Identification System based on Face using Active Horizontal Levels (AHLs) Feature

by Manhal S. Almohammad, Gouda I. Salama, Tarek A. Mahmoud
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 20
Year of Publication: 2013
Authors: Manhal S. Almohammad, Gouda I. Salama, Tarek A. Mahmoud
10.5120/10197-4972

Manhal S. Almohammad, Gouda I. Salama, Tarek A. Mahmoud . Human Identification System based on Face using Active Horizontal Levels (AHLs) Feature. International Journal of Computer Applications. 61, 20 ( January 2013), 27-32. DOI=10.5120/10197-4972

@article{ 10.5120/10197-4972,
author = { Manhal S. Almohammad, Gouda I. Salama, Tarek A. Mahmoud },
title = { Human Identification System based on Face using Active Horizontal Levels (AHLs) Feature },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 20 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 27-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number20/10197-4972/ },
doi = { 10.5120/10197-4972 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:08.867962+05:30
%A Manhal S. Almohammad
%A Gouda I. Salama
%A Tarek A. Mahmoud
%T Human Identification System based on Face using Active Horizontal Levels (AHLs) Feature
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 20
%P 27-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays, face is a crucial field for many pattern recognition researchers. It is considered as a good way for biometric authentication in many surveillance systems. The most important issue in face recognition is the features extraction from the face's images of the person's images or videos. In this paper, a proposed method has been introduced to identify person images, which are captured by cameras. This method depends on Active Horizontal Levels (AHLs) feature. Gain ratio attribute (feature) selection has been used to choose the Horizontal Levels (HLs) that lead to the highest identification rate. The proposed method was evaluated against BioID, UK, ORL and FEI face database, to recognize person from one image. The experimental results reveal the effectiveness of our proposed method against other face recognition methods to achieve better accuracies.

References
  1. K. Jain, R. Bolle and S. Pankanti, Biometrics: Personal Identification in Networked Security, Ed. Kluwer Academic Publishers, 1999.
  2. X. He, S. Yan, Y. Hu, P. Niyogi and H. Zhang. "Face Recognition using Laplacian Faces," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328–340, 2005.
  3. J. Fagertun. "Face Recognition," M. Eng. Thesis, Technical University on Denmark, 2005.
  4. R. Jafri and H. Arabnia. "A Survey of Face Recognition Techniques," Journal of Information Processing Systems (JIPS), vol. 5, no. 2, pp. 41-68, June 2009.
  5. Z. Liposcak and S. Loncaric. "A Scale-Space Approach to Face Recognition from Profiles," in Proc. of the 8th Int. Conf. on Computer Analysis of Images and Patterns, vol. 1689, pp. 243-250, London, UK, 1999.
  6. D. Swets and J. Weng, "Using Discriminant Eigenfeatures for Image Retrieval," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 18, pp. 831-836, August 1996.
  7. A. Nefian and M. Hayes. "Face Recognition using an Embedded HMM," in IEEE Int. Conf. Audio Video Biometric based Person Authentication (AVBPA), pp. 19 24, March, 1999.
  8. J. Haddadnia, M. Ahmadi and K. Faez, "An Efficient Method for Recognition of Human Face Recognition using Higher Order Pseudo Zernike Moment Invariant," in the 5th IEEE Int. Conf. on Automatic Face and Gesture Recognition. pp. 315-320, Washington, DC, USA, May 2002.
  9. A. Jain, R. Duin and J. Mao, "Statistical Pattern Recognition: A Review," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 22, no. 1, pp. 4-37, January 2000.
  10. G. Gordon, "Face Recognition Based on Depth Maps and Surface Curvature," in SPIE Proc. , Geometric Methods in Computer Vision, vol. 1570, pp. 234-247, September 1991.
  11. R. Cutler, "Face Recognition using Infrared Images and EgenFaces," CSC Tech. Rep. 989, University of Maryland at College Park, MD, USA, 1996.
  12. I. Dagher, "Incremental PCA-LDA Algorithm," 2nd Ed, Int. Journal of Biometrics and Bioinformatics (IJBB), vol. 4, no. 2, 2010.
  13. J. Cleary and L. Trigg. "K*: An Instance-based Learner using an Entropic Distance Measure," 12th Int. Conf. on Machine Learning, pp. 108-114, Tahoe City, CA, USA, 1995.
  14. E. Vasconcellos, R. De Carvalho, R. Gal, F. LaBarbera, H. Capelato, H. Campos Velho, M. Trevisan and R. Ruiz, "Decision Tree Classifiers for Star/Galaxy Separation," The Astronomical Journal, Cornell University arXiv: 1011. 1951, vol. 141, no. 6, November 2010.
  15. R. Datta and S. Sanjib. "An Empirical Comparison of Rule Based Classification Techniques in Medical Databases," Indian Institute of Foreign Trade, Indian, 2011.
  16. (2012) The Face databases website. Available: http://www. face-rec. org/databases/.
  17. M. Almohammad, G. Salama and T. Mahmoud, "Human Identification System Based on Face using Active Lines Feature among Face Landmark Points," ESC Journal, vol. 36, no. 3, September, 2012.
Index Terms

Computer Science
Information Sciences

Keywords

Face Identification Feature Extraction Biometric Authentication and Gain Ratio Attribute Selection