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Reseach Article

Implementation of Face Detection System using Adaptive Boosting Algorithm

by Khizer Mehmood, Basit Ahmad
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 76 - Number 2
Year of Publication: 2013
Authors: Khizer Mehmood, Basit Ahmad
10.5120/13223-0639

Khizer Mehmood, Basit Ahmad . Implementation of Face Detection System using Adaptive Boosting Algorithm. International Journal of Computer Applications. 76, 2 ( August 2013), 51-57. DOI=10.5120/13223-0639

@article{ 10.5120/13223-0639,
author = { Khizer Mehmood, Basit Ahmad },
title = { Implementation of Face Detection System using Adaptive Boosting Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2013 },
volume = { 76 },
number = { 2 },
month = { August },
year = { 2013 },
issn = { 0975-8887 },
pages = { 51-57 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume76/number2/13223-0639/ },
doi = { 10.5120/13223-0639 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:44:53.299359+05:30
%A Khizer Mehmood
%A Basit Ahmad
%T Implementation of Face Detection System using Adaptive Boosting Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 76
%N 2
%P 51-57
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Face detection is a very hot research topic in the fields of pattern recognition and computer vision. Its applications are widely used in artificial intelligence, surveillance video, identity authentication and human machine interaction. Face detection is based on identifying and locating a human face in the image, regardless of position, size, and condition. Various algorithms are proposed to detect faces in an image. This implementation is based on adaptive boosting algorithm and uses haar features which is based on statistical methods to detect face. Algorithm is implemented in MATLAB and synthesized by using Verilog on XILINX.

References
  1. G. Yang and T. S. Huang, "Human Face Detection in Complex Background," Pattern Recognition, vol. 27, no. 1, pp. 53-63, 1994.
  2. K. C. Yow and R. Cipolla, "A Probabilistic Framework for Perceptual Grouping of Features for Human Face Detection," Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, pp. 16-21, 1996.
  3. A. Lanitis, C. J. Taylor, and T. F. Cootes, "An Automatic Face Identification System Using Flexible Appearance Models," Image and Vision Computing, vol. 13, no. 5, pp. 393-401, 1995.
  4. K. -K. Sung and T. Poggio, "Example-Based Learning for View- Based Human Face Detection," IEEE Trans. Pattern Analysis andMachine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
  5. M. Turk and A. Pentland, "Eigenfaces for Recognition," J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991
  6. K. -K. Sung and T. Poggio, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
  7. H. Rowley, S. Baluja, and T. Kanade, "Neural Network-Based Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence,vol. 20, no. 1, pp. 23-38, Jan. 1998.
  8. E. Osuna, R. Freund, and F. Girosi, "Training Support Vector Machines: An Application to Face Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 130-136, 1997.
  9. H. Schneiderman and T. Kanade, "Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition,"Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 45-51,1998.
  10. A. Rajagopalan, K. Kumar, J. Karlekar, R. Manivasakan, M. Patil, U. Desai, P. Poonacha, and S. Chaudhuri, "Finding Faces in Photographs," Proc. Sixth IEEE Int'l Conf. Computer Vision, pp. 640-645, 1998.
  11. M. S. Lew, "Information Theoretic View-Based and Modular Face Detection," Proc. Second Int'l Conf. Automatic Face and Gesture Recognition, pp. 198-203, 1996.
  12. P. Viola and M. Jones, "Robust real-time object detection," International Journal of Computer Vision, 57(2), 137-154, 2004.
  13. K. Irick, M. DeBole, V. Narayanan, R. Sharma, H. Moon, and S. Mummareddy, "A unified streaming architecture for real time face detection and gender classification," in Proc. Int. Conf. Field Programmable Logic Appl. , Aug. 2007, pp. 267–272
  14. N. Farrugia, F. Mamalet, S. Roux, F. Yang, and M. Paindvoine, "Fast and robust face detection on a parallel optimized architecture implemented on FPGA," IEEE Trans. Circuits Syst. Video Technol. , vol. 19, no. 4, pp. 597–602, Apr. 2009.
  15. C. Gao and S. Lu, "Novel FPGA based Haar classifier face detection algorithm acceleration," in Proc. Int. Conf. Field Programm. Logic Appl. , Sep. 2008, pp. 373–378.
  16. Seunghun Jin; Dongkyun Kim; Thuy Tuong Nguyen; Daijin Kim; Munsang Kim; Jae Wook Jeon; , "Design and Implementation of a Pipelined Datapath for High-Speed Face Detection Using FPGA," Industrial Informatics, IEEE Transactions on , vol. 8, no. 1, pp. 158-167, Feb. 2012
  17. C. He, A. Papakonstantinou, and D. Chen, "A novel SoC architec-ture on FPGA for ultra-fast face detection," in Proc. IEEE Int. Conf. Comput. Design, Oct. 2009, pp. 412–418
  18. M. Hiromoto, K. Nakahara, and H. Sugano, "A specialized processor suitable for AdaBoost-based detection with Haar-like features," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , Oct. 2007, pp. 1–8.
  19. H. Ngo, R. Tompkins, J. Foytik, and V. Asari, "An area efficient modular architecture for real-time detection of multiple faces in video stream," in Proc. 6th Int. Conf. Inf. , Commun. Signal Process. , 2007,pp. 1–5.
  20. H. -C. Lai, M. Savvides, and T. Chen, "Proposed FPGA hardware architecture for high frame rate (> 100 fps) face detection using featurecascade classifiers," in Proc. IEEE Int. Conf. Biometr. : Theory, Appl. , Syst. , Sep. 2007, pp. 1–6.
  21. "The Face Database from Ohio State University. " [Online]. Available: http://www2. ece. ohiostate. edu/~aleix/ARdatabase. html
Index Terms

Computer Science
Information Sciences

Keywords

Face Detection Computer Vision Adaboost