Liu Chongwen, Qin Huafeng, Song Qun, Yan Huyong, Luo Fen
College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, China.
Chongqing Key Laboratory of Intelligent Perception and BlockChain Technology, Chongqing Technology and Business University, Chongqing, China.
Front Neurorobot. 2023 Jan 11;16:1065099. doi: 10.3389/fnbot.2022.1065099. eCollection 2022.
Finger-vein biometrics has been extensively investigated for personal verification. Single sample per person (SSPP) finger-vein recognition is one of the open issues in finger-vein recognition. Despite recent advances in deep neural networks for finger-vein recognition, current approaches depend on a large number of training data. However, they lack the robustness of extracting robust and discriminative finger-vein features from a single training image sample. A deep ensemble learning method is proposed to solve the SSPP finger-vein recognition in this article. In the proposed method, multiple feature maps were generated from an input finger-vein image, based on various independent deep learning-based classifiers. A shared learning scheme is investigated among classifiers to improve their feature representation captivity. The learning speed of weak classifiers is also adjusted to achieve the simultaneously best performance. A deep learning model is proposed by an ensemble of all these adjusted classifiers. The proposed method is tested with two public finger vein databases. The result shows that the proposed approach has a distinct advantage over all the other tested popular solutions for the SSPP problem.
手指静脉生物识别技术已被广泛研究用于个人身份验证。每人单样本（SSPP）手指静脉识别是手指静脉识别中的开放性问题之一。尽管最近在用于手指静脉识别的深度神经网络方面取得了进展，但当前的方法依赖大量训练数据。然而，它们缺乏从单个训练图像样本中提取鲁棒且有区分力的手指静脉特征的鲁棒性。本文提出了一种深度集成学习方法来解决SSPP手指静脉识别问题。在所提出的方法中，基于各种独立的深度学习分类器，从输入的手指静脉图像生成多个特征图。研究了分类器之间的共享学习方案以提高其特征表示能力。还调整了弱分类器的学习速度以实现同时的最佳性能。通过所有这些调整后的分类器的集成提出了一个深度学习模型。所提出的方法在两个公共手指静脉数据库上进行了测试。结果表明，对于SSPP问题，所提出的方法相对于所有其他测试的流行解决方案具有明显优势。
