Edge Detection and Template Matching Approaches for Human Ear Detection
K V Joshi and N C Chauhan. Edge Detection and Template Matching Approaches for Human Ear Detection. IJCA Special Issue on Intelligent Systems and Data Processing, pages 50-55, 2011. Full text available. BibTeX
@article{key:article,
author = {K. V. Joshi and N. C. Chauhan},
title = {Edge Detection and Template Matching Approaches for Human Ear Detection},
journal = {IJCA Special Issue on Intelligent Systems and Data Processing},
year = {2011},
pages = {50-55},
note = {Full text available}
}
Abstract
Ear detection is a new class of relatively stable biometrics which is not affected by facial expressions, cosmetics, eye glasses and aging effects. Ear detection is the first step of an ear recognition system, to use ear biometrics for human identification. In this paper, we have presented two approaches to detect ear from 2D side face images. One is edge detection based method and the other is template matching method. For both the methods, the correctness of the detected ear is verified using support vector machine tool. For template matching method it is also verified by Euclidian distance. The purpose of the paper is also to compare the results of both the presented methods. The experimental results prove the effectiveness of these methods.
Reference
- B V Srinivasan “Ear Extraction From the Image of a Human Face”. University of Maryland, College Park.
- H. Chen and B. Bhanu, “Hman Ear Detection From 3D Side Face Range Images”. 3D Imaging for Safety and Security, vol.35, Springer-2007, pp.133-155.
- S. M. S. Islam, M. Bennamoun and R. Davies, “Fast and Fully Automatic Ear Detection Using Cascaded AdaBoost” Proc. of IEEE Workshop on Application of Computer, 2008.
- S. Prakash, U. Jayaraman and P. Gupta, “A Skin-Color and Template Based Technique For Automatic Ear Detection”. Proc. Int’l Conf. Advances in Pattern Recognition, ICAPR' 09, Feruary 2009,pp. 213-216.
- K. Joshi, and N. Chauhan, “An Ear Detection and Support Vector Machine based Approach for Human Ear Detection and Varification”, Int. Conf. Intellignet Systems and Data Processing (ICISD-2011), G. H. Patel College of Engineering and Technology, Gujarat, India, 24-25 January, 2011.
- SL Phung, A. Bouzerdoum , D.Chai, “ Skin Segmentation Using Color Pixel Classification: Analysis and Comparison”, IEEE trans. on Pattern Analysis and Machine Intelligence,vol.27,no.1, January 2005, pp.148-154.
- V. Vezhnevets, V. Sazonov, A. Andreeva: “A Survey on Pixel-Based Skin Color Detection Techniques” International Journal of Expert Systems With Applications, vol.3, April 2009, pp 4497-4507.
- J. D. Foley, A. Dam, S. K. Feiner, J. F .Hughes “Computer Graphics Principles and Practice”, Pearson Education, 2006.
- R. C. Gonzalez and R. E. Woods, “Digital Image Processing”, Pearson Education, 2002.
- N. C. Chauhan, Y. K. Roy, Arun Kumar, A. Mittal, and M. V. Kartikeyan “ SVM-PSO Based Modeling and Optimization of Microwave Components”, Frequenz 62,2008.
- J. A. Suykens, J. Vandewalle, and B. D. Moor, “Optimal Control by Least Squares Support Vector Machines”, International Journal of Neural Networks,vol.14, no.1, Jan,2001, pp. 23-35.
- C. C. Chang and C. J. Lin, “LIBSVM – A Library for Support Vector Machines”, 2001.
- K. Joshi, and N. Chauhan, “A Template Matching and Support Vector Machine based Approach for Human Ear Detection and Varification”, Int. Conf. Information, Signals, and Communications (ICISC-2011), A. D. Patel Institute of Technology, Gujarat, India, 5-6 February, 2011.