Fingerprint recognition on mobile devices is an important method for identity
verification. However, real fingerprints usually contain sweat and moisture
which leads to poor recognition performance. In addition, for rolling out
slimmer and thinner phones, technology companies reduce the size of recognition
sensors by embedding them with the power button. Therefore, the limited size of
fingerprint data also increases the difficulty of recognition. Denoising the
small-area wet fingerprint images to clean ones becomes crucial to improve
recognition performance. In this paper, we propose an end-to-end trainable
progressive guided multi-task neural network (PGT-Net). The PGT-Net includes a
shared stage and specific multi-task stages, enabling the network to train
binary and non-binary fingerprints sequentially. The binary information is
regarded as guidance for output enhancement which is enriched with the ridge
and valley details. Moreover, a novel residual scaling mechanism is introduced
to stabilize the training process. Experiment results on the FW9395 and
FT-lightnoised dataset provided by FocalTech shows that PGT-Net has promising
performance on the wet-fingerprint denoising and significantly improves the
fingerprint recognition rate (FRR). On the FT-lightnoised dataset, the FRR of
fingerprint recognition can be declined from 17.75% to 4.47%. On the FW9395
dataset, the FRR of fingerprint recognition can be declined from 9.45% to
1.09%.

本研究提出了一种面向移动设备的端到端可训练的渐进式引导多任务神经网络（PGT-Net），通过清理小区域湿指纹图像以改善识别性能，将二进制信息作为输出增强的指导，结合了脊和谷细节。实验证明 PGT-Net 在湿指纹去噪和指纹识别率（FRR）方面具有良好表现，可将指纹识别率显著降低。