Department of MCA, Siddaganga Institute of Technology, Tumkur, Karnataka, IndiaEar biometrics have emerged as a reliable and non-invasive method for personal identification, offering distinct advantages in terms of stability and ease of acquisition. This study explores the application of Support Vector Machines (SVM) for the classification of ear biometric data, aiming to enhance the accuracy and efficiency of identification systems. By leveraging a comprehensive dataset of ear images, we employed SVM techniques to classify individual ear patterns, addressing challenges such as variability in image quality and alignment.

The research focuses on optimizing SVM parameters to improve classification performance, comparing linear and nonlinear kernels to determine the most effective approach. Our results demonstrate a significant improvement in classification accuracy, with SVM proving to be a robust method for ear biometric identification. This study highlights the potential of advanced SVM techniques in developing more reliable and efficient biometric systems, contributing to the broader field of biometric security.

Ear Biometrics, Support Vector Machines, Classification

Iannarelli A. “Ear Identification”, Forensic Identification Series, Paramount Publishing Company, Fremont, California;1989.

Chang K, Bowyer KW, SarkerS, Victor B. Comparison and Combination of Ear and Face Machine Image in appearance –Based Biometrics.IEEE Transaction on Pattern Analysis and Machine Intelligence. 2003;25(9):1160-1165.

MirHA, RubabS, ZhatZA.Biometrics verification: Aliterature survey.International Journal of Computing and ICT Research. 2010;5(2):67-80.

Shylaja D, Gupta P. A simple geometric approach for ear recognition, 9thInternational Conference on Information Technology, December 18-21, Bhuvaneswar, India. 2006;164-167.

PflugA, BuschC.Ear biometrics: A Survey of detection, feature extraction and recognition methods. IET Biometrics. 2012;1(2):114-129.

Narendra Kumar VK, SrinivasanB.Automated human identification scheme using ear biometrics technology. International Journal of Image, Graphics and Signal Processing (IJIGSP). 2014;6(3):58-65.

Sukhdeep Singh, Sunil Kumar Singla.A Review on biometrics and ear recognition techniques.International Journal of Advanced Research in Computer Science and Software Engineering. 2013;3(6): 1624-1630.

WANG Zhi-Qin, Tangshan, Yan Xiao Dong.Multi-scale feature extraction algorithm of ear image.International Conference on Electic Information and Control Engineering.Wuhan. 2011;528-531.

Jittendra B. Jawale, Smt. Anjali S. Bhalchandra.Ear based attendance monitoring system.International Conference on Emerging Treands in Electrical and Computer Technology, March 23-24, 2011, Tamilnadu, India. 2011;724-727.

Mahbubur Rahman.Person identification using ear biometrics.International Journal of the Computer, the Internet and Management.2007;1-8.

VapnikV. The Nature of Statistical Learning Theory. Springer, N.Y.;1995. ISBN 0-387-94559-8.

Burges C.A tutorial on support vector machines for pattern recognition.In “Data Mining and Knowledge Discovery”. Kluwer Academic Publishers, Boston.1998;2.

VapnikV, GolowichS, SmolaA. Supportvector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems. Cambridge, MA. MIT Press.1997;9:281-287.

Umadevi.Sentiment analysis using Weka, International Journal of Engineering Trends and Technology (IJETT). 2014;18(4):181-183.

Edson JR, Justino, Flavio Bortolozz, Robert Sabourin.A comparison of SVM and HMM classifier in the offline signature verification, Pattern recognition Letters. 2005;26(9):1377-1385.