CFP last date
22 April 2024
Reseach Article

Human Face Detection Technique using Haar-like Features

by Srikanta Pal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 32
Year of Publication: 2020
Authors: Srikanta Pal
10.5120/ijca2020920883

Srikanta Pal . Human Face Detection Technique using Haar-like Features. International Journal of Computer Applications. 175, 32 ( Nov 2020), 56-60. DOI=10.5120/ijca2020920883

@article{ 10.5120/ijca2020920883,
author = { Srikanta Pal },
title = { Human Face Detection Technique using Haar-like Features },
journal = { International Journal of Computer Applications },
issue_date = { Nov 2020 },
volume = { 175 },
number = { 32 },
month = { Nov },
year = { 2020 },
issn = { 0975-8887 },
pages = { 56-60 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number32/31661-2020920883/ },
doi = { 10.5120/ijca2020920883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:40:08.848790+05:30
%A Srikanta Pal
%T Human Face Detection Technique using Haar-like Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 32
%P 56-60
%D 2020
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Research on biometrics has become most popular topic nowadays. Face is one of the most important biometric traits that plays a crucial role in our social association, passing on individuals’ identity. Face detection is very important and challenging topic in the field of biometric research. It is also an essential step of any face recognition system. The actual benefits of face-based recognition or identification are uniqueness and acceptance. Face recognition or identification also has distinct advantages over other biometric systems because of its non-contact process while capturing the facial image. Face images are captured from a distance without any touch to the person who is being identified and the entire identification process does not require interacting with the person. Face recognition is a method of identifying people throughout facial images and it has many practical applications in the area of biometrics, information security, access control, smart cards, law enforcement and surveillance system. Therefore, face detection/recognition is one of the most interesting and important topics in research field. The goal of this study is to explore the face detection system using conventional face detection techniques using Haar-like feature and Haar-cascade classifier using OpenCv library. An encouraging average accuracy of 95.16% was achieved in this experiment.

References
  1. R Senthamizh Selvi , D. Sivakumar, J.S. Sandhya, S Siva Sowmiya, S Ramya, S Kanaga Suba Raja. “Face Recognition Using Haar - Cascade Classifier for Criminal Identification “, International Journal of Recent Technology and Engineering (IJRTE) , volume-7, issue-6S5, April 2019, pp. 1871-1876.
  2. Varun Garg and Kritika Garg, “Face Recognition Using Haar Cascade Classifier”, Journal of Emerging Technologies and Innovative Research (JETIR), volume 3, issue 12, pp. 140-142.
  3. Li Cuimei, Qi Zhiliang, “Human face detection algorithm via Haar cascade classifier with three additional classifiers”, International Conference on Electronic Measurement & Instruments, pp. 01-03, 2017.
  4. Souhail Guennouni and Anass Mansouri.“Face Detection: Comparing Haar-like combined with Cascade Classifiers and Edge Orientation Matching”, International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), pp. 02-04, 2017.
  5. C. Papageorgiou, M. Oren, and T. Poggio. A general framework for object detection. In International Conference on Computer Vision, 1998.
  6. J. Shah, M. Sharif, M. Raza, and A. Azeem, “A survey: linear and nonlinear PCA based face recognition techniques,” International Arab Journal of Information Technology, vol. 10, no. 6, pp. 536–545, 2013.
  7. C. Messom and A. Barczak, “Fast and efficient rotated haarlike features using rotated integral images,” in Proceedings of the Australasian Conference on Robotics and Automation (ACRA ’06), pp. 1–6, December 2006.
  8. P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition,pp. I511–I518, December 2001.
  9. S. Guennouni, A. Ahaitouf, and A. Mansouri, “Multiple object detection using OpenCV on an embedded platform,” in Proceedings of the 3rd IEEE International Colloquium in Information Science and Technology (CIST '14), pp. 374–377, IEEE, Tetouan, Morocco, October 2014.
  10. R. Leinhart and J. Maydt, “An extended set of haar-like features for rapid object detection”, Proceedings of the International Conference on Image Processing (ICIP '02), vol. 1, pp. I-900–I-903, 2002.
  11. F. C. Crow, “Summed-area tables for texture mapping”, Proceedings of the 11th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH ’84), pp. 207–212, 1984.
  12. Q. F. Zheng,W. Zeng, G.Wen, and W.Q.Wang, “Shape-based adult images detection,” in Proceedings of the 3rd International Conference on Image andGraphics, pp. 150–153,December 2004.
  13. P. Viola and M. J. Jones, “Robust real-time face detection”, International Journal of Computer Vision, vol.57, no.2, pp.137–154, 2004.
Index Terms

Computer Science
Information Sciences

Keywords

Haar-like features face detection cascade classifier region of interest.