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arXiv 1902.02176cs.CL
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一个线性复杂度的多生物特征法医文件分析系统，融合了样式和签名模式

A Linear-complexity Multi-biometric Forensic Document Analysis System, by Fusing the Stylome and Signature Modalities

Iran University of Science and Technology（伊朗科技大学）；Ferdowsi University of Mashhad（马什哈德菲尔多西大学）；University of Girona（赫罗纳大学）；University of California, Berkeley（加州大学伯克利分校）；University of Sassari（萨萨里大学）；University of Silesia（西里西亚大学）
Sayyed-Ali Hossayni, Yousef Alizadeh-Q, Vahid Tavana, Seyed M. Hosseini Nejad, Mohammad-R Akbarzadeh-T, Esteve Del Acebo, Josep Lluis De la Rosa i Esteva, Enric… 展开作者

Sayyed-Ali Hossayni, Yousef Alizadeh-Q, Vahid Tavana, Seyed M. Hosseini Nejad, Mohammad-R Akbarzadeh-T, Esteve Del Acebo, Josep Lluis De la Rosa i Esteva, Enrico Grosso, Massimo Tistarelli, Przemyslaw Kudlacik

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摘要：

法医文件分析（FDA）解决了查找给定文件的作者的问题。通过多种形式（如笔迹、签名、语言写作风格（即文体）等）识别文件撰写人已经在FDA的最新技术水平中进行了研究。但是，没有对花柱和特征模态的融合进行研究。在本文中，我们提出了这样一个双峰FDA系统（在司法，警察相关的，和历史文件分析中有着广泛的应用），重点是时间复杂度。所提出的双模系统可以以线性时间复杂度进行训练和测试。为了这个目的，我们首先重新审视多项式朴素贝叶斯（MNB），作为最好的国家的最先进的线性复杂度的作者归属系统，然后，证明其优越的准确性，以著名的线性复杂度分类器在国家的最先进的。然后，我们提出了一个模糊版本的MNB融合与国家的最先进的著名的线性复杂度模糊签名识别系统。为了评估的目的，我们构建了一个嵌合数据集，由不同信件的签名和文本内容组成。尽管其线性复杂性，所提出的多生物特征识别系统被证明是有意义地改善其国家的最先进的单峰同行，关于准确性，F分数，检测错误权衡（DET），累积匹配特性（CMC），和匹配分数直方图（MSH）的评价指标。

英文摘要：

Forensic Document Analysis (FDA) addresses the problem of finding the authorship of a given document. Identification of the document writer via a number of its modalities (e.g. handwriting, signature, linguistic writing style (i.e. stylome), etc.) has been studied in the FDA state-of-the-art. But, no research is conducted on the fusion of stylome and signature modalities. In this paper, we propose such a bimodal FDA system (which has vast applications in judicial, police-related, and historical documents analysis) with a focus on time-complexity. The proposed bimodal system can be trained and tested with linear time complexity. For this purpose, we first revisit Multinomial Naïve Bayes (MNB), as the best state-of-the-art linear-complexity authorship attribution system and, then, prove its superior accuracy to the well-known linear-complexity classifiers in the state-of-the-art. Then, we propose a fuzzy version of MNB for being fused with a state-of-the-art well-known linear-complexity fuzzy signature recognition system. For the evaluation purposes, we construct a chimeric dataset, composed of signatures and textual contents of different letters. Despite its linear-complexity, the proposed multi-biometric system is proven to meaningfully improve its state-of-the-art unimodal counterparts, regarding the accuracy, F-Score, Detection Error Trade-off (DET), Cumulative Match Characteristics (CMC), and Match Score Histograms (MSH) evaluation metrics.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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