| Abstrakt: |
The identification of individuals through finger vein patterns has become a prominent biometric technique due to its non-invasiveness and uniqueness. Convolutional neural networks (CNNs) have been at the forefront of this technology, offering impressive recognition rates within large, labeled datasets. Despite their successes, the application of CNNs to finger vein recognition remains a challenging task, largely due to the high dimensionality of input data and the multitude of classification outputs required. This paper presents an optimized CNN model designed to address the intricacies of finger vein image classification. It is posited that increasing the number of feature extraction layers, coupled with a strategic selection of kernel sizes for each layer, significantly enhances model accuracy. Through a series of systematic experiments, the optimal layer configurations were identified, resulting in an architecture that surpasses previous models in classification precision. The proposed CNN architecture demonstrates a classification accuracy exceeding 99%, an improvement over existing method. It is noteworthy that the development of this model has been constrained by the limited scale of current finger vein databases, which poses risks of overfitting. Hence, the expansion of these databases is suggested as a future avenue to reinforce the robustness of the training process. The results depicted in this study underscore the potential of deep learning techniques in biometric security, with the advanced CNN model setting a new benchmark in finger vein recognition. [ABSTRACT FROM AUTHOR] |
