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Fully automated human finger vein binary pattern extraction-based double optimization stages of unsupervised learning approach | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 187، دوره 13، شماره 2، مهر 2022، صفحه 2311-2323 اصل مقاله (1.12 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2022.27527.3638 | ||
| نویسندگان | ||
| Ali Salah Hameed* ؛ Adil Al-Azzawi | ||
| Department of Computer Science, College of Science, University of Diyala, Baquba, Iraq | ||
| تاریخ دریافت: 11 اسفند 1400، تاریخ بازنگری: 21 فروردین 1401، تاریخ پذیرش: 27 اردیبهشت 1401 | ||
| چکیده | ||
| 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). | ||
| کلیدواژهها | ||
| Clustering Algorithms؛ Unsupervised Learning؛ K-mean؛ FCM؛ Finger Vein Identification | ||
| مراجع | ||
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