فهرست نشریات علمی دسترسی آزاد ایران

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 labelled. 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 automatically. 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. In the second optimization, the retrieved finger vein lines are optimized. Lastly, the proposed system has a pattern extraction accuracy of 99.6\%, which is much higher than other common unsupervised learning methods like k-means and Fuzzy C-Means (FCM).