| Title |
A Palm Vein Recognition System based on a Support Vector Machine |
| Authors |
Vijayakumar Ponnusamy(Vijayakumar Ponnusamy) ; Abhijit Sridhar(Abhijit Sridhar) ; Arun Baalaaji(Arun Baalaaji) ; M. Sangeetha(M. Sangeetha) |
| DOI |
https://doi.org/10.5573/IEIESPC.2019.8.1.001 |
| Keywords |
Biometric identification ; Palm vein recognition ; Ridge filter ; Local binary patterns (LBP) ; Support vector machine (SVM) |
| Abstract |
Palm vein authentication is among the recent research in the field of access control applications. Because palm veins are tough to forge, they act as a reliable metric in security applications. Accurate extraction of palm veins is challenging in the presence of various dynamics, such as variant light conditions, variations in palm vein patterns from person to person, the cleanliness of the hand, etc. This paper presents a robust recognition process that makes use of a ridge filter for vein pattern extraction, and local binary patterns (LBP) for feature extraction. The ridge filter takes the major eigenvalue of the Hessian matrix, which contains the second-order derivative of the image pixels. The eigenvalue is then processed using LBP feature extraction from the vein patterns. Finally, a support vector machine is used for classification of subsequent images. The result shows that the system can provide accuracy of 89%, with a computation time of 0.423 s. The false acceptance rate and false rejection rate were also evaluated as benchmark parameters, which show significantly good performance. |