用深度学习精准分割无接触指纹图像中的指尖，提升生物识别准确率
TipSegNet: Fingertip Segmentation in Contactless Fingerprint Imaging
- 基于ResNeXt-101与特征金字塔网络，多尺度捕捉指尖特征
- 在复杂背景下实现mIoU 0.987、准确率0.999的顶尖表现
- 适合需要高精度无接触指纹识别的安防与移动设备场景
无接触指纹识别系统提供了比传统接触式方法更卫生、便捷且高效的选择。然而其准确性高度依赖于在复杂背景条件下对指尖的精确检测与分割。本文提出TipSegNet，一种新型深度学习模型，直接从灰度手部图像中实现指尖分割的最新性能。该模型采用ResNeXt-101作为骨干网络进行鲁棒特征提取，并结合特征金字塔网络（FPN）实现多尺度表征，从而在不同手指姿态和图像质量下保持高精度分割。此外，我们还采用大规模数据增强策略以提升模型泛化能力与鲁棒性。实验表明，TipSegNet在多个基准上显著优于现有方法，达到0.987的平均交并比（mIoU）和0.999的准确率，为无接触指纹分割带来显著进步。这一高精度有望大幅提升真实场景中无接触生物识别系统的可靠性与有效性。
原文摘要 · Abstract (English)
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.
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