使用VGG16和ResNet50进行指纹伪造检测的双模型协同作用
Mohamed Cheniti1, Zahid Akhtar2, Praveen Kumar Chandaliya3
1Faculty of Electrical Engineering, Telecommunications Department, Laboratory (LTIR), University of Science and Technology Houari Boumediene, BP.32, EI-Alia, Bab-Ezzouar, Algiers 16111, Algeria.
Journal of imaging
|February 25, 2025
概括
本研究介绍了一种使用VGG16和ResNet50进行双重预训练的模型,用于强大的指纹活力检测. 与单一模型方法相比,组合方法显著提高了准确性,并降低了错误率.
科学领域:
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 指纹活力检测对于安全至关重要.
- 使用单特征提取器的现有方法在一般化方面遇到了困难.
- 不同的伪造材料和传感器类型带来了重大挑战.
研究的目的:
- 开发一个先进的指纹生命检测系统.
- 为了增强各种伪造攻击的概括能力.
- 提高生物识别安全系统的准确性和可靠性.
主要方法:
- 一种双重预训练模型方法,结合了VGG16和ResNet50架构.
- 来自VGG16 (高分辨率) 和ResNet50 (深层) 的特征连接.
- 使用集成功能将指纹分类为真实或伪造的.
主要成果:
- 实现了最先进的准确性:在Livedet2013上达到99.72%,在Livedet2015上达到96.32%.
- 已证明的低错误率:真诚呈现分类错误率 (BPCER) 为0.28% (LivDet 2013) 和1.45% (LivDet 2015).
- 对未知的假冒材料观察到更高的攻击呈现分类错误率 (APCER) (例如,在交叉匹配子集上为8.12%).
结论:
- 双模型框架提供了强大的和可适应的指纹活力检测.
- 该方法有效地捕捉了全面的特征,以改进伪造检测.
- 需要进一步优化,以解决未见伪造材料的性能问题.
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