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An Approach to Face Detection and Feature Extraction using Canny Method

by Ranjana Sikarwar, Pradeep Yadav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 163 - Number 4
Year of Publication: 2017
Authors: Ranjana Sikarwar, Pradeep Yadav
10.5120/ijca2017913492

Ranjana Sikarwar, Pradeep Yadav . An Approach to Face Detection and Feature Extraction using Canny Method. International Journal of Computer Applications. 163, 4 ( Apr 2017), 1-5. DOI=10.5120/ijca2017913492

@article{ 10.5120/ijca2017913492,
author = { Ranjana Sikarwar, Pradeep Yadav },
title = { An Approach to Face Detection and Feature Extraction using Canny Method },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 163 },
number = { 4 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume163/number4/27380-2017913492/ },
doi = { 10.5120/ijca2017913492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:09:12.486643+05:30
%A Ranjana Sikarwar
%A Pradeep Yadav
%T An Approach to Face Detection and Feature Extraction using Canny Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 163
%N 4
%P 1-5
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper presents a hybrid approach to face detection and feature extraction. The remarkable advancement in technology has enhanced the use of more accurate and precise methods to detect faces. This paper presents a combination of three well known algorithms Viola- Jones face detection framework, Neural Networks and Canny edge detection method to detect face in static images. The proposed work emphasizes on the face detection and identification using Viola-Jones algorithm which is a real time face detection system. Neural Networks will be used as a classifier between faces and non-faces. Canny edge detection method is an efficient method for detecting boundaries on a face in this proposed work. The Canny edge detector is primarily useful to locate sharp intensity changes and to find object boundaries in an image.

References
  1. Zulhadi Zakaria"Face Detection Using Combination of Neural Network and Adaboost" Intelligent Biometric Group School of Electrical and Electronics Engineering Universiti Sains Malaysia.
  2. S.P.Khandait, Dr. R.C.Thool, "Hybrid Skin Detection Algorithm for Face localization in facial Expression Recognition", Proceedings of international conference on Advance computing conference-09 (IACC- 09), Patiala, Punjab, India, 6-7 March'09.
  3. Henry A. Rowley, Shumeet Baluja, and Takeo Kanade. Neural network based face detection. IEEE Trans. Pattern Anal. Mach. Intell., 20(1):23–38, 1998.
  4. H. A. Rowley, S. Baluja, and T. Kanade. Human face detection in visual scenes. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo, editors, Advances in Neural Information Processing Systems, volume 8, pages 875–881. The MIT Press, 1996.
  5. Kotropoulos, C. Pitas, I, “Rule-based face detection in frontal views” Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on Volume 4, 21-24 April 1997 Page(s):2537 – 2540 vol.4.
  6. Leung, T.K.; Burl, M.C.; Perona, P., “Finding faces in cluttered scenes using random labeled graph matching”,Computer Vision, 1995. Proceedings., Fifth International Conference on 20-23 June 1995 Page(s):637 – 644.
  7. Sung,K.-K, Poggio T, “Example based learning for view based human face detection” Pattern Analysis and Machine Intelligence, IEEE transactions on, volume 0,Issue 1 Jan 1998, Pages( 39 – 51).
  8. H. Schneiderman and T.Kanade. Probablistic modelling of local appearance and spatial relationships for object recognition. In Computer Vision and Pattern Recognition Conference 1998, 1998.
  9. Rowley, HA:, Baluja, S:, Kanade, T:, “Neural Network-Based face Detection”Computer Vision and Pattern Recognition, 1996 Proceedings CVPR96, 1996 IEEE Computer Society Conference on, 18-20 June 1996, pages 203-208.
  10. Rowley, HA:, Baluja, S:, Kanade, T:, “Rotation invariant Neural Network-Based face Detection”Computer Vision and Pattern Recognition, 1998. Proceedings. 1998 IEEE Computer Society Conference on 23-25 June 1998 Page(s):963 – 963.
  11. http://homepages.inf.ed.ac.uk/rbf/CVonline/low/edges/canny.htm S Price, "Edges: The Canny Edge Detector", July 4, 1996.
  12. Cristinacce, D., Cootes, T., and Scott, I. (2004). A multistage approach to facial feature detection. In 15th British Machine Vision Conference, 231-240.
  13. X. Wang and X. Tang, "Face Photo-Sketch Synthesis and Recognition," IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31, 2009.
  14. Extract of Facial Feature Point, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.1, January 2009
  15. Neural Network Based Approach for Face Detection cum Face Recognition, World Academy of Science, Engineering and Technology.
  16. Face detection, Inseong Kim, Joon Hyung Shim, and Jinkyu Yang.
  17. Robust Face Detection Method Based on Skin Color and Edges, J Inf Process Syst, Vol.9, No.1, March 2013. Detecting Boundaries for Segmentation and Recognition.
  18. Phil Brimblecombe (2002) “Face Detection using Neural Networks”, H615 – Meng Electronic Engineering, School of Electronics and Physical Sciences, URN: 1046063.
  19. Lixin Fan kah-kay sung, "Model -based varying pose face detection and facial feature registration in color images" 2002.
Index Terms

Computer Science
Information Sciences

Keywords

MLFFN FAR FFR HIT RATE CANNY