PGT-Net：用于小面积湿指纹去噪与识别的渐进引导多任务神经网络
PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition
AI总结：
针对小面积湿指纹识别困难，提出端到端PGT-Net，通过共享与多任务阶段、二值信息引导及残差缩放实现去噪增强，显著降低FRR。
摘要：
移动设备上的指纹识别是身份验证的重要方法。然而，真实指纹通常含有汗液和湿气，这会导致识别性能较差。此外，为了推出更纤薄的手机，科技公司通过将识别传感器嵌入电源按钮来缩小其尺寸。因此，指纹数据尺寸有限也增加了识别难度。将小面积湿指纹图像去噪为干净图像，对于提升识别性能变得至关重要。本文提出一种可端到端训练的渐进引导多任务神经网络（PGT-Net）。PGT-Net包含一个共享阶段和多个特定多任务阶段，使网络能够依次训练二值指纹和非二值指纹。二值信息被视为输出增强的引导，其中富含脊线和谷线细节。此外，本文引入一种新的残差缩放机制以稳定训练过程。在FocalTech提供的FW9395和FT-lightnoised数据集上的实验结果表明，PGT-Net在湿指纹去噪方面表现出良好性能，并显著提高了指纹识别率（FRR）。在FT-lightnoised数据集上，指纹识别的FRR可从17.75%降至4.47%。在FW9395数据集上，指纹识别的FRR可从9.45%降至1.09%。
英文摘要：
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%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
