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.

本研究解决了无接触指纹识别系统中指尖检测与分割精度不足的问题。提出的TipSegNet模型利用ResNeXt-101架构结合特征金字塔网络(FPN)，实现高精度的指尖分割，且在不同指姿和图像质量下表现出色。通过显著提高指尖分割的准确性，TipSegNet为无接触生物识别系统的可靠性和有效性带来了重要影响。

TipSegNet：无接触指纹成像中的指尖分割