Parametic Classification of Handvein Patterns Based on Texture Features
This work addresses biometric security by enhancing handvein recognition accuracy, but it is incremental as it combines existing methods without introducing new algorithms.
The paper tackled handvein biometric recognition by extracting texture features using LBP, LPQ, and Log-Gabor descriptors and classifying with KNN and SVM, achieving improved performance through feature-level fusion.
In this paper, we have developed Biometric recognition system adopting hand based modality Handvein, which has the unique pattern for each individual and it is impossible to counterfeit and fabricate as it is an internal feature. We have opted in choosing feature extraction algorithms such as LBP-visual descriptor ,LPQ-blur insensitive texture operator, Log-Gabor-Texture descriptor. We have chosen well known classifiers such as KNN and SVM for classification. We have experimented and tabulated results of single algorithm recognition rate for Handvein under different distance measures and kernel options. The feature level fusion is carried out which increased the performance level.