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Today, finger vein identification is gaining popularity as a potentialbiometric identification framework solution. Machine learning-basedunsupervised, supervised, and deep learning algorithms have had a significantinfluence on finger vein detection and recognition at the moment. Deeplearning, on the other hand, necessitates a large number of training datasetsthat must be manually produced and labeled. In this research, we offer acompletely automated unsupervised learning strategy for training datasetcreation. Our method is intended to extract and build a decent binary masktraining dataset completely automated. In this technique, two optimizationsteps are devised and employed. The initial stage of optimization is to createa completely automated unsupervised image clustering based on finger vein imagelocalization. Worldwide finger vein pattern orientation estimation is employedin the second optimization to optimize the retrieved finger vein lines.Finally, the proposed system achieves 99.6 - percent pattern extractionaccuracy, which is significantly higher than other common unsupervised learningmethods like k-means and Fuzzy C-Means (FCM).

Fully Automated Binary Pattern Extraction For Finger Vein Identification using Double Optimization Stages-Based Unsupervised Learning Approach