CFP last date
22 April 2024
Reseach Article

Real Time Multiple Face Detection Algorithm based on Minimum Facial Features

by Mohit Jain, Kishore Sethy, Gaurav Singh
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 138 - Number 8
Year of Publication: 2016
Authors: Mohit Jain, Kishore Sethy, Gaurav Singh
10.5120/ijca2016909004

Mohit Jain, Kishore Sethy, Gaurav Singh . Real Time Multiple Face Detection Algorithm based on Minimum Facial Features. International Journal of Computer Applications. 138, 8 ( March 2016), 21-25. DOI=10.5120/ijca2016909004

@article{ 10.5120/ijca2016909004,
author = { Mohit Jain, Kishore Sethy, Gaurav Singh },
title = { Real Time Multiple Face Detection Algorithm based on Minimum Facial Features },
journal = { International Journal of Computer Applications },
issue_date = { March 2016 },
volume = { 138 },
number = { 8 },
month = { March },
year = { 2016 },
issn = { 0975-8887 },
pages = { 21-25 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume138/number8/24399-2016909004/ },
doi = { 10.5120/ijca2016909004 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:39:09.209383+05:30
%A Mohit Jain
%A Kishore Sethy
%A Gaurav Singh
%T Real Time Multiple Face Detection Algorithm based on Minimum Facial Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 138
%N 8
%P 21-25
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, we have used the knowledge-based approach to detect the multiple human face of an image. We presented an algorithm, which detects human face by the geometric correlations between location of face and hairs in an image. Range of skin color are used to figure out possible face regions so as to initially localize the face, furthermore, the probable hair blocks in an image frame are determined by means of hair color spectrums. Combined skin and hair blocks decide candidate face areas in light of the geometric relation then we separate each component on the basis of pixel and count the number of faces detected therefore, the proposed is able to be expectedly transplanted to an embedded system, like the developing pet robot so as to perform dynamic face detection and tracking. The algorithm can be used for surveillance. The algorithm can be used for developing secure PC camera and web camera. The algorithm is being used for providing laptop security.

References
  1. Jerome, M. S.hapiro, 1993. “Embedded Image Coding Using Zerotress of Wavelet Coefficients”, IEEE Transaction on Signal Processing Vol.41No.12.
  2. T.K.Leung, M.C.Burl,and P.Perona, 1995“ Finding Face in Cluttered Scenes Using Random Labeled Graph Matching”, Fifth IEEE Int’l Conf.Computer Vision, 1995, pp637-644..
  3. Y. Dai and Y.Nakano, 1996 “Face-Texture Model Based on SGLD and Its Application in Face Detection in a Color Scence” , Pattern Recognition,vol. 29,no. 6, 1996, pp.1007-1017.
  4. J. Yang and A.Waibel, 1996 “A Real-Time Face Tracker” , Third Workshop Applications of Computer Vision, 1996,pp. 142-147.
  5. I. Craw, D. Tock, and A. 1992 Bennett, Proc. Second European Conf. Computer Vision, 1992, pp. 92-96. “Finding Face Features,”
  6. A. Lanitis,C.J. Taylor,and T.F. Cootes,. 1995 “An Automatic Face Identification System Using Flexible Appearance Models” , Image andVision Computing,vol. 13,no. 5, 1995, pp. 393-401.
  7. M. Turk and A.Pentland, 191 ”Eigenface for Recognition, “J.Cognitive Neuroscience,vol.3, 1991, pp.71-86.
  8. H. Rowley,S. Baluja,and T. Kanade, 1998 “Neural Network-Based Face Detection,”IEEE Trans. Pattern Analysis and Matchine Intelligence,vol.20,no.1,Jan. 1998.
  9. A. Rajagopalan,K. Kumar,J.Karlekar,R. Manivasakan,M. Patil,U. Desai, P. Poonacha, and S. Chaudhuri, 1998 “Finding Faces in Photographs” ,Proc. Sixth IEEE Int, l Conf. Computer Vision.
  10. Linda G. Shapiro, George C. Stockman , 2001 “Computer Vision” , Prentice Hall , pp.192-193.
  11. M. Soriano, S. Huovinen, B. Martinkauppi, and M. Laaksonen, 2000“Using the Skin Locus to Cope with Changing Illumination Conditions in Color-Based Face Tracking,” Proc. of IEEE Nordic Signal Processing Symposium, pp. 383-386.
  12. Rafael C. Gonzalez, Richard E. Woods, 2002 “Digital Image Processing”, 2nd Edition, Prentice Hall , pp.299-300.
  13. William K. Pratt, 2001 “Digital Image Processing”, 3rd Edition, John Willey & Sons , pp.63-87.
  14. Ramesh Jain, Rangachar Kasturi, Brian G. Schunck, 1995 “Machine Vision”, McGraw-Hill ,pp.44-48.
  15. Viola, Paul A. and Jones, Michael J ,2001 "Rapid Object Detection using a Boosted Cascade of Simple Features", IEEE CVPR.
  16. Yao-Jiunn Chen and Yen-Chun Lin, 2007 “Simple Face-detection Algorithm Based onMinimum Facial Features”, IEEE Industrial Electronics Society (IECON) Nov. 5-8.
  17. Oge Marques,2011 “ Practical Image and Video Processing using MATLAB”, A John Wiley & Sons Inc Publication, pp.561-578.
Index Terms

Computer Science
Information Sciences

Keywords

Skin Quantization Hair Quantization