PDF(Pubmed)
Abstract：
Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.
摘要：
指纹是用作生物特征密钥的独特模式，因为它们允许明确地识别个人，使它们在法医领域的应用成为一种普遍的做法。设计一个可以匹配不同图像细节的系统仍然是一个开放的问题，特别是当应用于大型数据库或，使用移动设备在取证场景中的实时应用程序。在犯罪现场收集的指纹通常被手动处理以找到与解决犯罪相关的指纹。这项工作提出了一种有效的方法，可以实时应用，以减少犯罪现场调查中消耗时间和人力资源的人工工作。所提出的方法包括四个步骤：（i）使用定向的Gabor滤波器进行图像预处理；（ii）使用交叉数字方法的变体提取细节点，该方法包括通过凸包和侵蚀进行新颖的ROI定义，然后用平均细节点替换两个或多个非常接近的细节点；（iii）创建一个模型，该模型通过一组多边形的特征来表示每个细节点，包括在每个图像的相对细节点上使用相邻细节点和每个细节点虽然在文献中，大多数方法都希望验证整个指纹模型，连接细节或使用细节三元组，我们使用n顶点多边形单独验证每个细节，这些多边形的顶点是围绕参考的相邻细节。我们的方法还揭示了针对虚假细节的鲁棒性，因为几个多边形用于表示相同的细节，即使有虚假的细节，真正的多边形存在并被识别；此外，我们的方法不受旋转和翻译的影响。结果表明,所提出的方法可以在标准硬件实现中实时应用,具有任意方向的图像。
