A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae

• URL: http://arxiv.org/abs/2504.15105v1
• Date: Mon, 21 Apr 2025 13:54:33 GMT
• Title: A triple-branch network for latent fingerprint enhancement guided by orientation fields and minutiae
• Authors: Yurun Wang, Zerong Qi, Shujun Fu, Mingzheng Hu,
• Abstract summary: Existing deep learning-based enhancement methods fall short of practical application requirements.<n>We propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies.<n> Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms.
• Score: 0.5356944479760104
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latent fingerprint enhancement is a critical step in the process of latent fingerprint identification. Existing deep learning-based enhancement methods still fall short of practical application requirements, particularly in restoring low-quality fingerprint regions. Recognizing that different regions of latent fingerprints require distinct enhancement strategies, we propose a Triple Branch Spatial Fusion Network (TBSFNet), which simultaneously enhances different regions of the image using tailored strategies. Furthermore, to improve the generalization capability of the network, we integrate orientation field and minutiae-related modules into TBSFNet and introduce a Multi-Level Feature Guidance Network (MLFGNet). Experimental results on the MOLF and MUST datasets demonstrate that MLFGNet outperforms existing enhancement algorithms.

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