ADVANCED EAR BIOMETRIC DATA CLASSIFICATION THROUGH SUPPORT VECTOR MACHINES

Аннотация

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. KEYWORDS Ear Biometrics, Support Vector Machines, Classification, Biometric Identification, Machine Learning, Pattern Recognition, Kernel Methods, Biometric Security, Image Processing, Feature Extraction.