Vein Pattern Visualisation and Feature Extraction using Sparse Auto-Encoder for Forensic Purposes

Soheil Varastehpour, H. Sharifzadeh, I. Ardekani, Xavier Francis
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引用次数: 5

Abstract

Child sexual abuse is a serious global problem that has gained public attention in recent years. Due to the popularity of digital cameras, many perpetrators take images of their sexual activities. Traditionally, it has been difficult to use vein patterns in evidence images for forensic identification, because they were nearly invisible in colour images. State-of-the-art techniques, and computational methods including optical-based vein uncovering or artificial neural networks have recently been introduced to extract vein patterns for identification purposes. However, these methods are still not mature due to limitations such as lack of reliable feature extraction, efficient uncovering algorithms, and matching difficulties. In this paper, we propose two new schemes to overcome some of these limitations by using sparse auto-encoder and adaptive contrast enhancement. Specifically, an adjustment sparse auto-encoder parameters scheme is used for optimising parameters, and then optimised parameters are automatically trained to enhance the robustness of vein visualisation and feature extraction. We also use a pair of synchronised colour and near infrared NIR images to generate the skeletonised vein patterns for verifying the outcome of the proposed method. The proposed algorithm was examined on a database with 100 pairs of colour and NIR images collected from different parts of the body such as forearms, thighs, chests and ankles. The experimental results are encouraging and indicate that the proposed method improves the feature extraction procedure, which can lead to better uncovering results compared with current methods.
基于稀疏自编码器的法医静脉模式可视化和特征提取
儿童性虐待是一个严重的全球性问题,近年来引起了公众的关注。由于数码相机的普及,许多犯罪者拍摄他们的性行为。传统上,利用证据图像中的静脉图案进行法医鉴定是很困难的,因为它们在彩色图像中几乎是不可见的。最近引入了最先进的技术和计算方法,包括基于光学的静脉揭示或人工神经网络来提取用于识别目的的静脉模式。然而,由于缺乏可靠的特征提取、高效的发现算法以及匹配困难等限制,这些方法仍然不成熟。在本文中,我们提出了两种新的方案,通过使用稀疏自编码器和自适应对比度增强来克服这些限制。具体而言,采用稀疏自编码器参数调整方案进行参数优化,并对优化后的参数进行自动训练,增强了静脉可视化和特征提取的鲁棒性。我们还使用一对同步的彩色和近红外近红外图像来生成骨架静脉图案,以验证所提出方法的结果。该算法在一个数据库中进行了检验,该数据库收集了100对来自身体不同部位(如前臂、大腿、胸部和脚踝)的彩色和近红外图像。实验结果令人鼓舞,表明该方法改进了特征提取过程,与现有方法相比,可以获得更好的发现结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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