  Today, finger vein identification is gaining popularity as a potential
biometric identification framework solution. Machine learning-based
unsupervised, supervised, and deep learning algorithms have had a significant
influence on finger vein detection and recognition at the moment. Deep
learning, on the other hand, necessitates a large number of training datasets
that must be manually produced and labeled. In this research, we offer a
completely automated unsupervised learning strategy for training dataset
creation. Our method is intended to extract and build a decent binary mask
training dataset completely automated. In this technique, two optimization
steps are devised and employed. The initial stage of optimization is to create
a completely automated unsupervised image clustering based on finger vein image
localization. Worldwide finger vein pattern orientation estimation is employed
in the second optimization to optimize the retrieved finger vein lines.
Finally, the proposed system achieves 99.6 - percent pattern extraction
accuracy, which is significantly higher than other common unsupervised learning
methods like k-means and Fuzzy C-Means (FCM).
