Minutiae extraction, a fundamental stage in fingerprint recognition, is increasingly shifting toward deep learning. However, truly end-to-end methods that eliminate separate preprocessing and postprocessing steps remain scarce. This paper introduces LEADER (Lightweight End-to-end Attention-gated Dual autoencodER), 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 complete end-to-end inference using only 0.9M parameters. It employs a novel "Castle-Moat-Rampart" ground-truth encoding and a dual-autoencoder structure, 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. Specifically, LEADER attains a 34% higher F1-score on the NIST SD27 dataset compared to specialized latent minutiae extractors. Sample-level analysis on this challenging benchmark reveals an average rank of 2.07 among all compared methods, with LEADER securing the first-place position in 47% of the samples-more than doubling the frequency of the second-best extractor. The internal representations learned by the model align with established fingerprint domain features, such as segmentation masks, orientation fields, frequency maps, and skeletons. Inference requires 15ms on GPU and 322ms on CPU, outperforming leading commercial software in computational efficiency. The source code and pre-trained weights are publicly released to facilitate reproducibility.

翻译：细节特征提取作为指纹识别的关键步骤，正日益向深度学习方向转变。然而，真正能够消除独立预处理与后处理步骤的端到端方法仍十分稀缺。本文提出LEADER（轻量级端到端注意力门控双自编码器），这是一种将原始指纹图像直接映射至细节特征描述符（包括位置、方向与类型）的神经网络。所提出的架构集成了非极大值抑制与角度解码机制，仅使用0.9M参数即可实现完整的端到端推理。该方法采用新颖的“城堡-护城河-城墙”真值编码策略及双自编码器结构，并通过注意力门控机制相互连接。实验评估表明，该方法在清晰指纹上达到最先进的精度，并对潜在指纹图像展现出强大的跨域泛化能力。具体而言，在NIST SD27数据集上，LEADER相比专用的潜在细节特征提取器获得了高出34%的F1分数。在该挑战性基准上的样本级分析显示，LEADER在所有对比方法中平均排名为2.07，并在47%的样本中取得首位——这一比例超过第二名提取器的两倍。模型学习到的内部表征与指纹领域已知特征（如分割掩码、方向场、频率图及骨架图）高度吻合。推理过程在GPU上仅需15毫秒，在CPU上需322毫秒，在计算效率上优于主流商业软件。源代码与预训练权重已公开发布，以促进可复现性研究。