TipSegNet:无接触指纹成像中的指尖细分
Laurenz Ruzicka1,2, Bernhard Kohn2, Clemens Heitzinger3
1Faculty of Physics, TU Wien, 1040 Vienna, Austria.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
TipSegNet可以准确地从手图片中分离指尖,以实现卫生无接触指纹识别. 这种深度学习模型通过在具有挑战性的条件下实现近乎完美的准确性,显著提高了生物识别系统的可靠性.
科学领域:
- 计算机科学 计算机科学
- 生物识别信息 生物识别信息
- 图像处理 图像处理
背景情况:
- 无接触式指纹识别比传统方法有优势,但需要精确的指尖细分.
- 由于手指姿势和背景条件的变化,准确的细分是具有挑战性的.
研究的目的:
- 介绍TipSegNet,这是一个新的深度学习模型,用于在非接触式生物识别中准确的指尖细分.
- 提高非接触式指纹识别系统的性能和稳定性.
主要方法:
- 开发了TipSegNet,这是一个使用ResNeXt-101骨干和特征金字塔网络 (FPN) 的深度学习模型.
- 采用了广泛的数据增强,以提高通用性.
- 在2257个标记的手图像的数据集上训练和评估模型.
主要成果:
- TipSegNet在指尖细分方面实现了最先进的性能.
- 该模型获得了0.987的平均交叉与结合 (mIoU) 和0.999.99的准确性.
- 与现有方法相比,表现出优越的性能.
结论:
- TipSegNet代表了无接触指纹细分技术的重大进步.
- 该模型的高精度可以大大提高现实世界无接触生物识别系统的可靠性.
相关概念视频
01:03IR Frequency Region: Fingerprint Region
645
IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
645
01:14Design Example: Resistive Touchscreen
245
A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
245