TipSegNet：非接触式指纹成像中的指尖分割
TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging
AI总结：
针对非接触式指纹识别中复杂背景下的指尖分割问题，提出融合 ResNeXt-101 与 FPN 的 TipSegNet，并通过数据增强实现 mIoU 0.987、准确率 0.999 的先进性能。
摘要：
非接触式指纹识别系统为传统的基于接触的方法提供了一种卫生、用户友好且高效的替代方案。然而，其准确性在很大程度上依赖于精确的指尖检测与分割，尤其是在具有挑战性的背景条件下。本文提出了 TipSegNet，这是一种新型深度学习模型，可直接从灰度手部图像中分割指尖，并实现了最先进的性能。TipSegNet 利用 ResNeXt-101 主干网络进行鲁棒的特征提取，并结合特征金字塔网络（FPN）实现多尺度表征，从而能够在不同手指姿态和图像质量下进行准确分割。此外，我们采用了广泛的数据增强策略，以提升模型的泛化能力和鲁棒性。TipSegNet 优于现有方法，取得了 0.987 的平均交并比（mIoU）和 0.999 的准确率，代表了非接触式指纹分割领域的重大进展。这种准确性的提升有可能在实际应用中显著提高非接触式生物特征识别系统的可靠性和有效性。
英文摘要：
Contactless fingerprint recognition systems offer a hygienic, user-friendly, and efficient alternative to traditional contact-based methods. However, their accuracy heavily relies on precise fingertip detection and segmentation, particularly under challenging background conditions. This paper introduces TipSegNet, a novel deep learning model that achieves state-of-the-art performance in segmenting fingertips directly from grayscale hand images. TipSegNet leverages a ResNeXt-101 backbone for robust feature extraction, combined with a Feature Pyramid Network (FPN) for multi-scale representation, enabling accurate segmentation across varying finger poses and image qualities. Furthermore, we employ an extensive data augmentation strategy to enhance the model's generalizability and robustness. TipSegNet outperforms existing methods, achieving a mean Intersection over Union (mIoU) of 0.987 and an accuracy of 0.999, representing a significant advancement in contactless fingerprint segmentation. This enhanced accuracy has the potential to substantially improve the reliability and effectiveness of contactless biometric systems in real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
