This paper introduces LEADER (Lightweight End-to-end Attention-gated Dual Encoder-decodeR), a neural network that maps raw fingerprint images to minutiae descriptors, including location, direction, and type. The proposed architecture integrates non-maximum suppression and angular decoding to enable end-to-end inference using only 0.9M parameters. It employs a novel ``Castle-Moat-Rampart'' ground-truth encoding and a dual encoder-decoder architecture, interconnected through an attention-gating mechanism. Experimental evaluations demonstrate state-of-the-art accuracy on plain fingerprints and robust cross-domain generalization to latent impressions across two benchmark datasets. Specifically, LEADER outperforms top-tier commercial systems, achieving F1-score gains of up to 34% and 39% over the closest competitor on NIST SD27 and SD302, respectively. Qualitative visual analysis suggests that the internal representations learned by the model align with established fingerprint domain concepts, such as segmentation masks, orientation fields, frequency maps, and skeletons. Inference requires only 15ms on GPU (average latency per image at batch size 32) and 322ms on CPU, outperforming leading commercial software in computational efficiency. The source code and pre-trained weights are publicly released to facilitate reproducibility.

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