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Projection pursuit and PCA associated with near and middle infrared hyperspectral images to investigate forensic cases of fraudulent documents

机译:投影追求和PCA与附近和中红外高光谱图像相关,以调查欺诈性文件的法制案例

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In forensic examination of questioned documents a type of casework often encountered are the frauds that occur by mean of addition and adulteration of parts of text or numbers on document. The goal of this work is to evaluate the performance of hyperspectral images (HSI) in the near (NIR) and middle (MIR) regions, combined with the unsupervised pattern recognition techniques Principal ComponentAnalysis (PCA) and Projection Pursuit (PP) for a rapid, reliable and non-destructive identification of document falsifications by means of alterations and additions. Blind tests were conducted for this purpose. Sixteen black ink pens from different brands, models and ink types were employed to prepare the samples in two different ways: (i) for initial discrimination and method validation, straight lines of approximately 2 cm long were produced in white paper; (ii) for blind testing, three collaborators used any of the sixteen pens available and prepared genuine or altered/added numbers (in total 30 samples) in white paper and in bank check paper. Overall, PP analysis showed better results than PCA to discriminate the 120 pairs of ink lines in white paper using HSI-MIR (97.5% and 87.5%, respectively). It is important to mention that the 10.0% of pairs that were not discriminated by PCA analysis were discriminated by PP, which highlights the importance of the combined use of the two chemometric techniques. HSI-NIR in combination with PCA and PP analysis was able to solve 76.7% and 833% of the blind testing samples, respectively. When HSI-MIR was used in a complementary way to HSI-NIR, discrimination of blind test samples increased to 90%. Therefore, HSI-NIR and HSI-MIR combined with PCA and PP show great discrimination potential and provide objective examination of suspected fraudulent documents. (C) 2016 Elsevier B.V. All rights reserved.
机译:在质疑文件的质量检查中,通常遇到的案例是欺诈,这些案例由文本或数字部分的补充和掺假的诉讼发生。这项工作的目标是评估附近(NIR)和中间(MIR)区域的高光谱图像(HSI)的性能,与无监督的模式识别技术主要成分(PCA)和投影追踪(PP)结合快速通过改变和添加方式可靠,无损识别文件伪造。为此目的进行了盲试验。来自不同品牌,模型和油墨类型的十六款黑色墨水钢笔以两种不同的方式制备样品:(i)用于初始辨别和方法验证,在白皮书中生产大约2厘米长的直线; (ii)对于盲检测,三个合作者使用了任何十六笔,并在白皮书和银行检查纸中准备了最真实或改变/添加的数字(总共30个样本)。总体而言,PP分析显示出比PCA的更好的结果,以使用HSI-MIR(分别为97.5%和87.5%)以鉴别白皮书中的120对墨水线。重要的是要提及由PP歧视不受PCA分析的10.0%的对,这突出了两个化学计量技术联合使用的重要性。 HSI-NIR与PCA和PP分析组合能够分别解决76.7%和833%的盲检测样品。当HSI-MIR以互补的方式使用到HSI-NIR时,盲目测试样品的歧视增加到90%。因此,HSI-NIR和HSI-MIR与PCA和PP相结合,显示出良好的歧视潜力,并提供客观审查涉嫌欺诈文件。 (c)2016年Elsevier B.v.保留所有权利。

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