论文标题
使用基于双优化阶段的无监督学习方法，全自动二进制模式提取用于手指静脉识别
Fully Automated Binary Pattern Extraction For Finger Vein Identification using Double Optimization Stages-Based Unsupervised Learning Approach
论文作者
论文摘要
如今，手指静脉识别已成为潜在的生物特征识别框架解决方案。目前，基于机器学习的无监督，监督和深度学习算法对手指静脉的检测和识别产生了重大影响。另一方面，深度学习需要大量必须手动生产和标记的培训数据集。在这项研究中，我们为培训数据集创建提供了完全自动化的无监督学习策略。我们的方法旨在提取和构建一个完全自动化的体面的二进制掩码训练数据集。在此技术中，设计和采用了两个优化步骤。优化的初始阶段是基于手指静脉图像定位创建完全自动化的无监督图像聚类。在第二次优化中采用了全球手指静脉模式取向估计，以优化检索到的手指静脉线。最后，所提出的系统达到了99.6-％的模式提取精度，其明显高于其他常见的无监督学习方法，例如K-均值和模糊C均值（FCM）。
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 labeled. 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 automated. 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. Worldwide finger vein pattern orientation estimation is employed in the second optimization to optimize the retrieved finger vein lines. Finally, the proposed system achieves 99.6 - percent pattern extraction accuracy, which is significantly higher than other common unsupervised learning methods like k-means and Fuzzy C-Means (FCM).
