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Reseach Article

Efficient Face Detection Method using Modified Hausdorff Distance Method with C4.5 Classifier and Canny Edge Detection

by Neelima Singh, Satish Pawar, Yogendra Kumar Jain
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 10
Year of Publication: 2015
Authors: Neelima Singh, Satish Pawar, Yogendra Kumar Jain
10.5120/ijca2015905553

Neelima Singh, Satish Pawar, Yogendra Kumar Jain . Efficient Face Detection Method using Modified Hausdorff Distance Method with C4.5 Classifier and Canny Edge Detection. International Journal of Computer Applications. 123, 10 ( August 2015), 38-44. DOI=10.5120/ijca2015905553

@article{ 10.5120/ijca2015905553,
author = { Neelima Singh, Satish Pawar, Yogendra Kumar Jain },
title = { Efficient Face Detection Method using Modified Hausdorff Distance Method with C4.5 Classifier and Canny Edge Detection },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 10 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 38-44 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number10/21998-2015905553/ },
doi = { 10.5120/ijca2015905553 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:12:22.645198+05:30
%A Neelima Singh
%A Satish Pawar
%A Yogendra Kumar Jain
%T Efficient Face Detection Method using Modified Hausdorff Distance Method with C4.5 Classifier and Canny Edge Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 10
%P 38-44
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the rapid growth of population and technology, security problems become a major issue due to abnormal human behaviors. Recently researches have been motivated towards automatic human face detection from still image or from moving image. Present human face detection system leads computation inaccuracies i.e. higher degree of false negative rate. In this paper, a multilevel hybrid model has been proposed for face detection. In the proposed work, we initially use C4.5 classifier so that foreground and background images can be differentiated, as a result of which search space can be reduced. After that skin color model has been applied to detect the skin region which is followed by canny edge detection to detect the edges of skin region. In the last step, we use the Modified Hausdorff Distance Method which matches the pixel values and detects the faces with lower false negative rate.

References
  1. Liu C., “A bayesian discriminating features method for face detection” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. (6), pp. 725-740, 2003.
  2. Kim H., Lee S., and Cho N., “Rotation-invariant face detection using angular projections” Electron. Lett. , vol. (40), no. 12, pp. 726- 727, 2004.
  3. Suzuki Y., and Shibata T., “Multiple-clue face detection algorithm using edge-based feature vectors” Proc. ICASSP, vol. (5) , pp. 737-740, 2004.
  4. Padma Polash Paul, Md. Maruf Monwar, Marina Gavrilova, and Patrick Wang, “Rotation Invariant Multi-view Face Detection Using Skin Color Regressive Model and Support Vector Regression”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 24, no. (8), pp. 1261-1280, 2010.
  5. Hongming Zhang, Debin Zhao, Wen Gao and Xilin Chen, “Combining Skin Color Model and Neural Network for Rotation Invariant Face Detection”, Springer-Verlag Berlin Heidelberg, vol. 1948, pp. 237-244, 2000.
  6. Rowley, H.A., Baluja, S., Kanade, T. “Neural network-based face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. (1), pp. 23 – 38, Jan. 1998.
  7. Sung, K.-K., Poggio, T.,“Example-based learning for view-based human face detection”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. (1), pp.39 – 51, Jan.1998.
  8. Hazem M. El-Bakry, “New fast principal component analysis for face detection”, Journal of Advanced Computational Intelligence and Intelligent Informatics, vol. 11, no.(2), pp. 195-201, 2007
  9. Yasmin Andreu, Ramon A. Mollineda and Pedro Garcia-Sevilla, “Gender Recognition from a Partial View of the Face Using Local Feature Vectors“, Pattern Recognition and Image Analysis Springer, vol. 5524, pp. 481–488, 2009.
  10. Seungmin Lee, Hogyun Lee, and Taekyong Nam “A Comparative Study of the Objectionable Video Classification Approaches Using Single and Group Frame Features”, ICANN Springer, pp. 616–623, 2006.
  11. Yongjin Kwona, M. K. Jeongb, O.A. Omitaomub, “Adaptive support vector regression analysis of closed-loop inspection accuracy”, International Journal of Machine Tools & Manufacture, Elsevier, vol. 46 no. (6), pp. 603–610, May 2006.
  12. Anima Majumder “Automatic and Robust Detection of Facial Features in Frontal Face Images” UKSim 13th International Conference on Modelling and Simulation , IEEE, pp. 331-336, April 2011.
  13. J. Kovac, P. Peer, F. Solina, “Illumination independent color-based face detection”, 3rd International Symposium on Image and Signal Processing and Analysis, IEEE, vol. 1, pp. 510-515, Sept 2003.
  14. Chiunhsiun Lin, “Face detection in complicated backgrounds and different illumination conditions by using YCbCr color space and neural network” Elsevier Transaction on Pattern Recognition Letters, vol. 28, no. (16), pp. 2190-2200, Dec 2007.
  15. Khalid Mohamed Alajel, Wei Xiang, and John Leis “Face Detection Based on Skin Color Modeling and Modified Hausdorff Distance” in 8th Annual IEEE Consumer Communications and Networking Conference, IEEE, pp.399-404, Jan 2011
  16. Padma Polash Paul , Marina Gavrilova “PCA Based Geometric Modeling for Automatic Face Detection” in International Conference on Computational Science and Its Applications , International Conference on ICCSA, IEEE, pp. 33-38, 2011.
  17. Kamarul Hawari Bin Ghazali, Jie Ma, Rui Xiao, Solly Aryza lubis, “An Innovative Face Detection Based on YCgCr Color Space”, Elsevier International Conference on Solid State Devices and Materials Science, vol. 25, pp. 2116-2124, April 2012.
  18. Ramirez, G. A. and Fuentes, Olac, “Face Detection Using Combinations of Classifiers”, the2nd Canadian Conference on Computer and Robot Vision, IEEE, pp. 610-615, May 2005.
  19. Reema Ajmera and Namrata Saxena, “Face Detection in Digital Images Using Color Spaces and Edge Detection Techniques”, IJARCSSE, vol. 3, no. (6), pp. 718-725, 2013.
  20. Albert Pujol and Juan Jose Villanueva, “A Supervised Modification Of The Hausdorff Distance For Visual Shape Classification”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 16, no. (3), pp. 349–359, 2002.
  21. Erdem Yoruk, Ender Konuko˘Glu, Bulent Sankur And Jerome Darbon, “Shape-Based Hand Recognition”, IEEE Transactions On Image Processing, vol. 15, no.(7), pp. 1803-1815, 2006.
  22. M. P. Dubuisson And A. K. Jain, “A Modified Hausdorff Distance For Object Matching” 12th Int. Conf. Pattern Recognition, pp. 566–568, 1994.
  23. A. Gallagher, T. Chen, “Understanding Groups of Images of People”, IEEE Conference on Computer Vision and Pattern Recognition, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

C4.5 Classifier Modified Hausdorff Distance YCbCr Color space model