Dense registration of fingerprints is a challenging task due to elastic skin distortion, low image quality, and self-similarity of ridge pattern. To overcome the limitation of handcraft features, we propose to train an end-to-end network to directly output pixel-wise displacement field between two fingerprints. The proposed network includes a siamese network for feature embedding, and a following encoder-decoder network for regressing displacement field. By applying displacement fields reliably estimated by tracing high quality fingerprint videos to challenging fingerprints, we synthesize a large number of training fingerprint pairs with ground truth displacement fields. In addition, based on the proposed registration algorithm, we propose a fingerprint mosaicking method based on optimal seam selection. Registration and matching experiments on FVC2004 databases, Tsinghua Distorted Fingerprint (TDF) database, and NIST SD27 latent fingerprint database show that our registration method outperforms previous dense registration methods in accuracy. Mosaicking experiments on FVC2004 DB1_A and a small fingerprint database demonstrate that the proposed algorithm produced higher quality fingerprints and led to higher matching accuracy, which also validates the performance of our registration algorithm.

中文翻译：

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通过训练端到端网络进行指纹的密集注册和镶嵌


由于皮肤弹性变形、图像质量低以及脊线图案的自相似性，指纹的密集配准是一项具有挑战性的任务。为了克服手工特征的限制，我们建议训练一个端到端网络来直接输出两个指纹之间的像素级位移场。所提出的网络包括用于特征嵌入的连体网络和用于回归位移场的编码器-解码器网络。通过将通过跟踪高质量指纹视频可靠估计的位移场应用于具有挑战性的指纹，我们合成了大量具有地面实况位移场的训练指纹对。此外，基于所提出的配准算法，我们提出了一种基于最优接缝选择的指纹镶嵌方法。在FVC2004数据库、清华扭曲指纹（TDF）数据库和NIST SD27潜在指纹数据库上的配准和匹配实验表明，我们的配准方法在准确性上优于以前的密集配准方法。在FVC2004 DB1_A和小型指纹数据库上进行的马赛克实验表明，所提出的算法产生了更高质量的指纹并带来了更高的匹配精度，这也验证了我们的配准算法的性能。