Research Article
Real Time Multiple Face detection from Live Camera, a Step towards Automatic Attendance System
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 45 - Issue 4 |
| Published: May 2012 |
| Authors: Raikoti Sharanabasappa, Sanjaypande M. B |
10.5120/6771-9057
|
Raikoti Sharanabasappa, Sanjaypande M. B . Real Time Multiple Face detection from Live Camera, a Step towards Automatic Attendance System. International Journal of Computer Applications. 45, 4 (May 2012), 45-49. DOI=10.5120/6771-9057
@article{ 10.5120/6771-9057,
author = { Raikoti Sharanabasappa,Sanjaypande M. B },
title = { Real Time Multiple Face detection from Live Camera, a Step towards Automatic Attendance System },
journal = { International Journal of Computer Applications },
year = { 2012 },
volume = { 45 },
number = { 4 },
pages = { 45-49 },
doi = { 10.5120/6771-9057 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2012
%A Raikoti Sharanabasappa
%A Sanjaypande M. B
%T Real Time Multiple Face detection from Live Camera, a Step towards Automatic Attendance System%T
%J International Journal of Computer Applications
%V 45
%N 4
%P 45-49
%R 10.5120/6771-9057
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Student attendance system is manual in most part of the world with essential Roll call and answering taking significant time. The objective of this work is to propose a model in openCV that captures live stream from camera and enables multiple face detection and segmentation. The segmented faces can further be used to recognize the students. As such the system leads towards the development of automatic attendance system, where the camera can be static and periodically can take the snap of the class. Further each image is processed to extract the faces. Haar cascade is used for face detection and Gaussian mixture model is used for face segmentation. A test over 1000 images reveals a result with 83% accuracy where accuracy is measured in terms of number of actual face detected v/s the number of faces present in a scene. The test are generated in various angles and light intensity.
References
- Chellappa, Wilson, Sirohey, "Human and Machine Recognition of Faces: A Survey", Proceedings of the IEEE, Vol. 83, No. 5, May 1995, pp. 705-740.
- M. Turk and A. P. Pentland, "Face recognition using eigenfaces", Proc. CVPR, 1991, pp 586-593.
- Menser, Wien, "Segmentation and Tracking of Facial Regions in Color Image Sequences", RWTH, Aachen, Germany, 1999.
- Jones, Rehg, "Statistical Color Models with Application to Skin Detection", Tech-Rep. CRL 98/11, Compaq Cambridge Research Lab 1998.
- Y. Raja, S. McKenna and S. Gong, "Colour Model Selection and Adaptation in Dynamic Scenes". In Proc. European Conference on Computer Vision, Freiburg, Germany, 2-6 June 1998.
- C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
- M. -H. Yang and N. Ahuja, "Gaussian Mixture Modeling of Human Skin Color and Its Applications in Image and Video Databases", In the 1999 SPIE/EI&T Storage and Retrieval for Image and Video Databases, pp. 458-466, San Jose, January, 1999 .
- R. A. Redner, and H. F. Walker, "Mixture densities, maximum likelihood and the EM algorithm", SIAM Review, Vol. 26, no. 2, pp. 195-239, 1984.
Index Terms
Keywords