fully automated human finger vein binary pattern extraction-based double optimization stages of unsupervised learning approach

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).