LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification

• URL: http://arxiv.org/abs/2511.08119v1
• Date: Wed, 12 Nov 2025 01:40:41 GMT
• Title: LatentPrintFormer: A Hybrid CNN-Transformer with Spatial Attention for Latent Fingerprint identification
• Authors: Arnab Maity, Manasa, Pavan Kumar C, Raghavendra Ramachandra,
• Abstract summary: Latent fingerprint identification is a challenging task due to low image quality, background noise, and partial impressions.<n>In this work, we propose a novel identification approach called LatentPrintFormer.<n>The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints.
• Score: 4.819347753965351
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Latent fingerprint identification remains a challenging task due to low image quality, background noise, and partial impressions. In this work, we propose a novel identification approach called LatentPrintFormer. The proposed model integrates a CNN backbone (EfficientNet-B0) and a Transformer backbone (Swin Tiny) to extract both local and global features from latent fingerprints. A spatial attention module is employed to emphasize high-quality ridge regions while suppressing background noise. The extracted features are fused and projected into a unified 512-dimensional embedding, and matching is performed using cosine similarity in a closed-set identification setting. Extensive experiments on two publicly available datasets demonstrate that LatentPrintFormer consistently outperforms three state-of-the-art latent fingerprint recognition techniques, achieving higher identification rates across Rank-10.

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