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International Journal of Nonlinear Analysis and Applications، جلد ۱۳، شماره ۲، صفحات ۲۳۱۱-۲۳۲۳
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عنوان انگلیسی Fully automated human finger vein binary pattern extraction-based double optimization stages of unsupervised learning approach
چکیده انگلیسی مقاله 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).
کلیدواژه‌های انگلیسی مقاله Clustering Algorithms, Unsupervised Learning, K-mean, FCM, Finger Vein Identification
نویسندگان مقاله Ali Salah Hameed |
Department of Computer Science, College of Science, University of Diyala, Baquba, Iraq

Adil Al-Azzawi |
Department of Computer Science, College of Science, University of Diyala, Baquba, Iraq

نشانی اینترنتی https://ijnaa.semnan.ac.ir/article_6625_c602c97dca787d004a2c712434396819.pdf
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