Improving interoperability in contactless-to-contact fingerprint matching is a crucial factor for the mainstream adoption of contactless fingerphoto devices. However, matching contactless probe images against legacy contact-based gallery images is very challenging due to the presence of heterogeneity between these domains. Moreover, unconstrained acquisition of fingerphotos produces perspective distortion. Therefore, direct matching of fingerprint features suffers severe performance degradation on cross-domain interoperability. In this study, to address this issue, the authors propose a coupled adversarial learning framework to learn a fingerprint representation in a low-dimensional subspace that is discriminative and domain-invariant in nature. In fact, using a conditional coupled generative adversarial network, the authors project both the contactless and the contact-based fingerprint into a latent subspace to explore the hidden relationship between them using class-specific contrastive loss and ArcFace loss. The ArcFace loss ensures intra-class compactness and inter-class separability, whereas the contrastive loss minimises the distance between the subspaces for the same finger. Experiments on four challenging datasets demonstrate that our proposed model outperforms state-of-the methods and two top-performing commercial-off-the-shelf SDKs, that is, Verifinger v12.0 and Innovatrics. In addition, the authors also introduce a multi-finger score fusion network that significantly boosts interoperability by effectively utilising the multi-finger input of the same subject for both cross-domain and cross-sensor settings.

中文翻译：

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利用耦合对抗学习提高跨域多指指纹匹配的互操作性

提高非接触式与接触式指纹匹配的互操作性是非接触式指纹拍照设备主流采用的关键因素。然而，由于这些域之间存在异质性，将非接触式探针图像与传统的基于接触式图库图像进行匹配非常具有挑战性。此外，无限制地获取手指照片会产生透视失真。因此，指纹特征的直接匹配在跨域互操作性方面会严重降低性能。在本研究中，为了解决这个问题，作者提出了一个耦合的对抗性学习框架来学习低维子空间中的指纹表示，该子空间本质上是有判别性和域不变性的。事实上，使用条件耦合生成对抗网络，作者将非接触式指纹和接触式指纹投影到潜在子空间中，以使用特定于类的对比损失和 ArcFace 损失来探索它们之间隐藏的关系。ArcFace 损失确保类内紧凑性和类间可分离性，而对比损失则最小化同一手指的子空间之间的距离。对四个具有挑战性的数据集的实验表明，我们提出的模型优于现有方法和两个性能最佳的商业现成 SDK，即 Verifinger v12.0 和 Innovatrics。此外，作者还介绍了一种多指评分融合网络，通过有效利用同一主题的多指输入进行跨域和跨传感器设置，显着提高互操作性。