PFVNet: A Partial Fingerprint Verification Network Learned From Large Fingerprint Matching
Zhiyuan He, Jun Zhang, Liaojun Pang, Eryun Liu
Abstract
With the decreasing size of fingerprint scanners in portable devices, e.g., mobile phone and smart watch, partial fingerprint recognition has become a challenging and urgently needed technique due to the limited features contained in small area as well as the large rotation and translation between query and reference images. Deep learning as a powerful modeling method has advanced the research progress of fingerprint recognition, but it still suffers from the lack of labeled data in the scenario of partial fingerprint matching. In this paper, we propose a novel partial fingerprint verification network (PFVNet) based on spatial transformer network (STN) and the local self-attention mechanism. Our model can be trained end-to-end and learn multi-level fingerprint features automatically. To alleviate the data annotation work, the model is trained in a self-supervision and domain adaptation manner with data generated from large fingerprint image matching. The experimental results compared with other methods on FVC2006 DB1 dataset and in-house datasets (i.e., ZJUPartial database) show that our method achieves state-of-the-art performance, and also robust to different types of scanners.