TY  - JOUR
T1  - Authentication using Palm Print Recognition System
AU - Tamije Selvy, P. AU - Anjugam, A. AU - Arifa Begum, P. AU - Arthi, V. 
JO  - International Journal of Soft Computing
VL  - 16
IS  - 4
SP  - 73
EP  - 77
PY  - 2021
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2021.73.77
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2021.73.77
KW  - Palm print
KW  -Preprocessing
KW  -Gray Level Co-occurrence Matrix (GLCM) and Support Vector Machine classification (SVM)
AB  - 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.
ER  -