Tang Chaoying, Zhang Yufeng, Han Liyuan, Chen Xiaoteng
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
J Forensic Sci. 2022 May;67(3):1002-1020. doi: 10.1111/1556-4029.15002. Epub 2022 Feb 8.
In forensic investigations, images of evidence can often be obtained from crimes such as child pornography and masked violent riots. However, identifying criminals is usually very difficult and sometimes impossible because these images usually contain skin of body parts, while their faces and other commonly used biometrics are unavailable. Vein patterns are a potential biometric to solve this problem. Traditional systems use near-infrared (NIR) imaging technologies to obtain vein patterns, which cannot be applied to forensic analysis since only RGB images are available. However, veins are unobservable in RGB images. In this paper, a comprehensive scheme including a vein uncovering algorithm, a vein extraction algorithm, and a vein pattern matching algorithm is presented. Based on the Monte Carlo (MC) simulation of light transmission in a skin optical model, physical parameters corresponding to different skin colors are obtained, and vein patterns are uncovered from the parameter distribution images. After preprocessing with cubic convolution and Gabor filtering, vein lines are extracted based on ridge tracking. Local gradient orientation and the geometric direction of veins are utilized to guarantee the correct tracking direction. Hessian-based Frangi filters are adopted to locate potential veins. In the matching step, effective minutiae are extracted to represent the topology of vein patterns. A modified coherent point drift (CPD) algorithm is proposed utilizing coordinates, Gabor energy values, and curvatures of minutiae to match vein patterns. Comprehensive experiments were carried out to evaluate the proposed three algorithms. Experimental results show the superiority of the proposed algorithms to various state-of-the-art methods.
在法医调查中，证据图像通常可从儿童色情和蒙面暴力骚乱等犯罪活动中获取。然而，识别罪犯通常非常困难，有时甚至不可能，因为这些图像通常包含身体部位的皮肤，而其面部和其他常用生物特征无法获取。静脉模式是解决这一问题的潜在生物特征。传统系统使用近红外（NIR）成像技术来获取静脉模式，但由于只有RGB图像可用，所以无法应用于法医分析。然而，静脉在RGB图像中是不可见的。本文提出了一种综合方案，包括静脉揭示算法、静脉提取算法和静脉模式匹配算法。基于皮肤光学模型中光传输的蒙特卡罗（MC）模拟，获得了对应不同肤色的物理参数，并从参数分布图像中揭示出静脉模式。经过三次卷积和Gabor滤波预处理后，基于脊线跟踪提取静脉线。利用局部梯度方向和静脉的几何方向来保证正确的跟踪方向。采用基于Hessian的Frangi滤波器来定位潜在静脉。在匹配步骤中，提取有效的细节点来表示静脉模式的拓扑结构。提出了一种改进的相干点漂移（CPD）算法，利用细节点的坐标、Gabor能量值和曲率来匹配静脉模式。进行了全面的实验来评估所提出的三种算法。实验结果表明，所提出的算法优于各种现有方法。
