Interoperability between contactless and conventional contact-based fingerprint recognition systems is fundamental for the success of emerging contactless fingerprint technologies which are highly sought, especially due to current pandemic. However, image formation differences and acquisition distortions between these two modalities pose significant challenges for such interoperability. In order to address these challenges, this paper presents a minutiae attention network with Siamese architecture and the reciprocal distance loss function to enable more accurate contactless to contact-based fingerprint identification. The proposed network contains two branches, a global-net branch to recover global features and a minutiae attention branch that focuses on the local minutiae areas. Attention mechanism is introduced to guide the minutiae attention branch to concentrate on distorted areas and recover minutiae/features correspondence for contactless and contact-based fingerprint images from the same fingers. Meanwhile, reciprocal distance loss is specifically designed to impose strong penalty towards contactless and contact-based fingerprint images from different fingers and guide the network to learn robust features for distinguishing identities. Experimental results on two publicly available databases illustrate significant performance improvements, over state-of-art methods in the literature, and validate the effectiveness of the proposed framework for the contactless to contact-based fingerprint identification.

中文翻译：

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用于非接触式到接触式指纹识别的具有倒数距离损失的细节注意网络


非接触式指纹识别系统和传统接触式指纹识别系统之间的互操作性是新兴非接触式指纹技术成功的基础，这些技术受到高度追捧，特别是由于当前的流行病。然而，这两种模式之间的图像形成差异和采集失真对这种互操作性提出了重大挑战。为了应对这些挑战，本文提出了一种具有暹罗架构的细节注意网络和倒数距离损失函数，以实现更准确的非接触式到接触式指纹识别。所提出的网络包含两个分支，一个用于恢复全局特征的全局网络分支和一个专注于局部细节区域的细节注意力分支。引入注意力机制来引导细节注意力分支集中于扭曲区域，并恢复来自同一手指的非接触式和接触式指纹图像的细节/特征对应关系。同时，倒数距离损失专门设计用于对来自不同手指​​的非接触式和接触式指纹图像施加强烈惩罚，并引导网络学习用于区分身份的鲁棒特征。两个公开数据库上的实验结果表明，与文献中最先进的方法相比，性能有了显着提高，并验证了所提出的非接触式指纹识别到接触式指纹识别框架的有效性。