| Popis: |
Dorsal hand vein patterns have gained prominence as a reliable biometric modality for verifying and identifying individuals. They offer a significant alternative to other well-known biometric modalities such as face, iris, or fingerprints, providing unique advantages in terms of security and privacy. While multiple researchers are engaged in this biometric research area, dorsal hand vein-based recognition systems, involving knuckle alignment and matching algorithms, exhibit a distinct set of limitations that motivated our work. Thus, this paper introduces an innovative knuckle alignment method tailored for processing near-infrared (NIR) imaging of dorsal hand vein images, aiming to enhance the precision and robustness of such a biometric system. Our newly proposed efficient knuckle alignment method addresses the variability in hand positioning during capturing. It utilizes the entire dorsal hand vein image without necessitating the extraction of specific regions of interest. Our knuckle alignment method ensures greater consistency across varied samples in terms of alignment accuracy and reduces intra-class variability, which significantly enhances the accuracy and reliability of the resulting biometric system. To evaluate the efficacy of our proposed approach, we conduct comparative analyses among automated, computer vision-based, and traditional manual-based alignment methods. Experimental results demonstrate that by standardizing image orientation, the proposed automated approach improves system performance. In addition, the proposed automated approach is expected to offer substantial benefits in terms of scalability and operational efficiency without compromising the high precision typically associated with manual techniques. Our method yields a verification accuracy score of 99.07% on the JLU dataset and 99.90% on the DHV dataset, which is higher than the competition. These findings underscore the potential of our proposed knuckle alignment method to serve as a valuable tool in the ongoing development and optimization of dorsal hand vein authentication systems. |
