Feature Points based Residual UNet with Nonlinear Decay Rate for Partial Wet Fingerprint Restoration and Recognition

This paper proposes FPN-ResUNet, which uses feature points-based restoration and nonlinear decay rate residual within a U-shape architecture to restore blurry and wet fingerprints within size constraints effectively. Our approach aims to restore fingerprints effectively while avoiding over-restoration and preserving local matching feature points, which reduces the false rejection rate (FRR). We develop novel residual blocks, which feature a nonlinear decay mechanism that optimizes weight allocation between blocks to enhance feature extraction capabilities. Furthermore, our residual feature points fusion module restores contextual information by fusing matching feature points and features from the previous level.Through comprehensive experiments on real-world data, we achieved a remarkable 9.4% reduction in FRR. Our method outperforms the basic U-Net framework [1] by 50.52% in overall performance. We improved 48.35% over FPDMNet [2] and 58.77% over DenseUNet [3] which is trained with small patches. Moreover, PGT-Net [4] is also used for small areas of wet fingerprints, but our FPNResUNet outperformed it by 72.51%.