Abstract: To address the challenge of accurately identifying high-fidelity silicone forged fingerprints in traditional morphological inspection,a local-global gated feature-level fusion network is proposed under the transfer learning framework for the automatic qualitative inspection of authentic and forged paper-based fingerprints.Aiming at the limitation that traditional recognition me-thods struggle to balance micro-texture and macro-smearing,the network takes a modern lightweight convolutional model as the backbone to extract stable local ridge features,while integrating a Patch convolution branch to capture cross-scale global morphological differences and consistency variations in ink diffusion.Adaptive fusion of local and global features is achieved through a gating mechanism.Experiments are conducted on 16 000 authentic and forged fingerprint samples collected from 20 volunteers using four types of media:red,blue,and black inkpad oils,as well as red inkpaste.The results demonstrate that the network maintains stable and high-precision recognition performance under multi-media and cross-individual conditions,with core metrics including accuracy,F1-score,and AUC overall outperforming traditional pattern recognition methods.Grad-CAM visualization results confirm that the model mainly focuses on regions such as ink diffusion boundaries,ridge fractures,and gray-scale abnormal bands,and its decision-making logic is highly consistent with the inspection experience of judicial examiners.This effectively improves the ability to distinguish subtle forged traces under complex media,providing a high-precision and interpretable technical approach for the intelligent detection of forged fingerprints in judicial identification.

Key words: Deep learning, Authentic and forged fingerprint identification, Feature-level fusion, Local-global gating, Transfer learning