PGT-Net: Progressive Guided Multi-task Neural Network for Small-area Wet Fingerprint Denoising and Recognition
Not on the map. Why?
Abstract (arXiv)
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%.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 31 references that are in the map. First posted on arXiv 14 Aug 2023.
Builds on
10 of 31 references.
References · 10
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
- 2017Enhanced Deep Residual Networks for Single Image Super-Resolutioncited by 1,552
- 2017Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semanticscited by 1,358
- 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 803
- 2016Recurrent Neural Network for Text Classification with Multi-Task Learningcited by 144
- 2016HyperFace: A Deep Multi-task Learning Framework for Face Detection, Landmark Localization, Pose Estimation, and Gender Recognitioncited by 104
- 2017Adversarial Multi-task Learning for Text Classificationcited by 91
- 2016FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomicscited by 17
- 2018FPD-M-net: Fingerprint Image Denoising and Inpainting Using M-Net Based Convolutional Neural Networkscited by 5
Built on by
Not cited yet.