论文标题
基于变压器的指纹特征提取
Transformer based Fingerprint Feature Extraction
论文作者
论文摘要
指纹特征提取是使用全局或局部表示求解的任务。最先进的全球方法使用大量深度学习模型一次处理完整的指纹图像，从而使相应的方法记忆密集型。另一方面，本地方法涉及基于细节的补丁提取，多个特征提取步骤和昂贵的匹配阶段，这使得相应的进近时间密集型。但是，这两种方法都为解决问题提供了有用的，有时甚至是独家见解。使用两种方法一起提取指纹表示，在语义上是有用的，但效率很低。我们采用内置小型萃取器的基于卷积变压器的方法为提取指纹的全局和局部表示提供了时间和记忆有效的解决方案。这些表示形式的使用以及智能匹配过程为我们提供了多个数据库的最先进的性能。项目页面可以在https://saraansh199999.github.io/global-plus-plus-local-fp-transformer上找到。
Fingerprint feature extraction is a task that is solved using either a global or a local representation. State-of-the-art global approaches use heavy deep learning models to process the full fingerprint image at once, which makes the corresponding approach memory intensive. On the other hand, local approaches involve minutiae based patch extraction, multiple feature extraction steps and an expensive matching stage, which make the corresponding approach time intensive. However, both these approaches provide useful and sometimes exclusive insights for solving the problem. Using both approaches together for extracting fingerprint representations is semantically useful but quite inefficient. Our convolutional transformer based approach with an in-built minutiae extractor provides a time and memory efficient solution to extract a global as well as a local representation of the fingerprint. The use of these representations along with a smart matching process gives us state-of-the-art performance across multiple databases. The project page can be found at https://saraansh1999.github.io/global-plus-local-fp-transformer.
