Finger-vein biometrics has recently gained significant attention due to its robust privacy and high security features. Despite notable advancements, most existing methods focus on extracting features from a 2-dimensional (2D) image projected from 3D vein vessels with a single view. However, recognition based on a single view is prone to errors due to variations in finger positioning, especially those caused by finger roll movements, which can degrade recognition performance. To address this challenge, we propose ABLSTM-TSVT, an Attention Bidirectional LSTM-based Temporal-Spatial Vein Transformer for multi-view finger-vein recognition. First, we enhance LSTM with an attention mechanism to create an attention LSTM for extracting temporal features. We further improve this by introducing a local attention module, which learns temporal dependencies between a patch (token) and its adjacent patches across multiple views, integrating it with the attention LSTM to form a temporal attention module. Second, we develop a spatial attention module that captures the spatial dependencies of patches within an image. Finally, merging the temporal and the spatial attention modules, we create our temporal-spatial transformer model, which effectively represents features from multi-view images. Experimental results on two multi-view datasets demonstrate that our approach outperforms state-of-the-art approaches in enhancing identification accuracy and reducing verification errors in vein classifiers.

中文翻译：

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基于 BLSTM 的时空脉转换器，用于多视图指静脉识别


指静脉生物识别技术由于其强大的隐私性和高安全性功能而最近受到了广泛关注。尽管取得了显着进步，但大多数现有方法都侧重于从具有单一视图的 3D 静脉血管投影的 2D （2D） 图像中提取特征。但是，由于手指位置的变化，尤其是由手指滚动移动引起的错误，基于单个视图的识别容易出现错误，这会降低识别性能。为了应对这一挑战，我们提出了 ABLSTM-TSVT，这是一种基于注意力双向 LSTM 的时间空间静脉转换器，用于多视图指静脉识别。首先，我们用注意力机制增强 LSTM，创建一个用于提取时间特征的注意力 LSTM。我们通过引入本地注意力模块进一步改进了这一点，该模块在多个视图中学习补丁（标记）与其相邻补丁之间的时间依赖关系，并将其与注意力 LSTM 集成以形成时间注意力模块。其次，我们开发了一个空间注意力模块，用于捕获图像中补丁的空间依赖性。最后，合并时间和空间注意力模块，我们创建了时间-空间转换器模型，它有效地表示了多视图图像中的特征。在两个多视图数据集上的实验结果表明，我们的方法在提高识别准确性和减少静脉分类器的验证错误方面优于最先进的方法。