Abstract：
Forensic identification using vein patterns in standard colour images presents significant challenges due to their low visibility. Recent efforts have employed various computational techniques, including artificial neural networks and optical vein disclosure, to enhance vein pattern detection. However, these methods still face limitations in reliability when compared to Near-Infrared (NIR) reference images. One of the biggest challenges of the studies is the limited number of available datasets that have synchronised colour and NIR images from body limbs. This paper introduces a new dataset comprising 602 pairs of synchronised NIR and RGB forearm images from a diverse population, ethically approved and collected in Auckland, New Zealand. Using this dataset, we also propose a conditional Generative Adversarial Networks (cGANs) model to translate RGB images into their NIR equivalents. Our evaluations focus on matching accuracy, vein length measurements, and contrast quality, demonstrating that the translated vein patterns closely resemble their NIR counterparts. This advancement offers promising implications for forensic identification techniques.
摘要：
使用标准彩色图像中的静脉图案的法医学鉴定由于其可见度低而提出了重大挑战。最近的努力采用了各种计算技术，包括人工神经网络和光学静脉披露，以增强静脉模式检测。然而,与近红外（NIR）参考图像相比，这些方法在可靠性方面仍然存在局限性。这项研究的最大挑战之一是有限的可用数据集，这些数据集具有同步的彩色和NIR图像。本文介绍了一个新的数据集，包括来自不同人群的602对同步NIR和RGB前臂图像，在奥克兰获得道德批准和收集，新西兰。使用此数据集，我们还提出了一个条件生成对抗网络（cGANs）模型，将RGB图像转换为它们的NIR等价物。我们的评估重点是匹配的准确性，静脉长度测量，和对比度质量，证明翻译的静脉模式与NIR相似。这一进步为法医鉴定技术提供了有希望的意义。
