Authors : P. Tamije Selvy, A. Anjugam, P. Arifa Begum and V. Arthi
Abstract: Personal authentication plays a unique role in the public security, access control, forensic and E-banking. Palm print recognition has been recognized as an effective biometric identifier because it is more reliable and user friendly. Existing methods used in palm print recognition lacks in reliability, accuracy and have higher error rate. The proposed scheme implements palm print recognition system using Gray Level Co-occurrence Matrix (GLCM) in feature extraction and Support Vector Machine (SVM) in classification. The proposed method not only uses the orientation features and also includes second order features like energy, correlation, contrast and homogeneity for recognition and comparison. It shows robustness to noise and rotation. It has a simple and effective balancing scheme to improve the precision of the orientation feature of the palm print. Experiments conducted on dataset demonstrate that proposed method give better results than existing orientation methods. The Proposed method enhances the accuracy and also it reduces the average error rate in classification.
P. Tamije Selvy, A. Anjugam, P. Arifa Begum and V. Arthi, 2021. Authentication using Palm Print Recognition System. International Journal of Soft Computing, 16: 73-77.