在新西兰数据集上使用cGAN进行生物识别的静脉模式可视化
Ali Keivanmarz1, Hamid Sharifzadeh1
1School of Computing, Unitec Institute of Technology, Auckland, New Zealand.
Forensic science international
|May 18, 2024
概括
这项研究引入了同步近红外 (NIR) 和RGB前臂图像的新数据集. 一个新的条件生成对抗网络 (cGAN) 模型将RGB图像转换为NIR,改善了法医静脉模式识别.
科学领域:
- 法医科学 法医科学 法医科学
- 生物识别信息 生物识别信息
- 计算机视觉 计算机视觉
背景情况:
- 对于法医识别而言,静脉模式识别受到标准色彩 (RGB) 图像中可见度低的挑战.
- 与近红外 (NIR) 成像相比,现有的神经网络和光脉揭示等计算方法在可靠性方面存在局限性.
- 对肢体的同步RGB和NIR图像数据集的稀缺性阻碍了研究进展.
研究的目的:
- 介绍一个新的,伦理认可的数据集602同步NIR和RGB前臂图像从多样化的人口.
- 提出一种有条件的生成对抗网络 (cGAN) 模型,用于将RGB图像转换为它们的NIR等效.
- 评估cGAN模型在提高静脉图案可见性和法医识别的准确性方面的有效性.
主要方法:
- 收集602个配对的NIR和RGB前臂图像的新数据集.
- 开发和实施一个有条件的生成对抗网络 (cGAN) 模型.
- 基于匹配精度,静脉长度测量和对比质量的翻译静脉模式的评估.
主要成果:
- 拟议的cGAN模型成功地将RGB图像转换为类似NIR的静脉图案.
- 翻译的静脉模式与实际的NIR对应物具有很高的相似性.
- 评估显示,在匹配精度,静脉长度一致性和改善对比质量方面取得了有希望的结果.
结论:
- 开发的数据集和cGAN模型为基于静脉模式的法医识别的研究提供了重大进展.
- 从RGB数据生成NIR等效图像的能力提高了使用标准成像进行可靠识别的潜力.
- 这种方法有望提高法医识别技术的准确性和可访问性.
相关概念视频
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Structure of Veins:
The structure of veins is specifically designed to assist in the low-pressure transportation of blood...
Structure of Veins:
The structure of veins is specifically designed to assist in the low-pressure transportation of blood...
01:10Veins as Blood Reservoirs
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01:18Veins of the Abdomen and Pelvis
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01:19Veins of Head and Neck
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01:17Veins of Upper Limbs
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01:15Veins of Lower Limbs
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