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首页> 外文会议>International Conference on Digital Printing Technologies; 20041031-1105; Salt Lake City,UT(US) >Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification
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Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification

机译：主成分分析和高斯混合模型在打印机识别中的应用

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摘要

Printer identification based on a printed document has many desirable forensic applications. In the electrophotographic process (EP) quasiperiodic banding artifacts can be used as an effective intrinsic signature. However, in text only document analysis, the absence of large midtone areas makes it difficult to capture suitable signals for banding detection. Frequency domain analysis based on the projection signals of individual characters does not provide enough resolution for proper printer identification. Advanced pattern recognition techniques and knowledge about the print mechanism can help us to device an appropriate method to detect these signatures. We can get reliable intrinsic signatures from multiple projections to build a classifier to identify the printer. Projections from individual characters can be viewed as a high dimensional data set. In order to create a highly effective pattern recognition tool, this high dimensional projection data has to be represented in a low dimensional space. The dimension reduction can be performed by some well known pattern recognition techniques. Then a classifier can be built based on the reduced dimension data set. A popular choice is the Gaussian Mixture Model where each printer can be represented by a Gaussian distribution. The distributions of all the printers help us to determine the mixing coefficient for the projection from an unknown printer. Finally, the decision making algorithm can vote for the correct printer. In this paper we will describe different classification algorithms to identify an unknown printer. We will present the experiments based on several different EP printers in our printer bank. The classification results based on different classifiers will be compared.
机译：基于打印文档的打印机识别具有许多理想的法证应用。在电子照相过程（EP）中，准周期性带状伪影可以用作有效的固有特征。但是，在纯文本文档分析中，由于缺少大的中间调区域，因此难以捕获合适的信号进行条带检测。基于各个字符的投影信号的频域分析不能提供足够的分辨率来正确识别打印机。先进的图案识别技术和有关打印机制的知识可以帮助我们使用适当的方法来检测这些签名。我们可以从多个投影中获得可靠的固有签名，以建立用于识别打印机的分类器。来自单个字符的投影可以视为高维数据集。为了创建高效的模式识别工具，必须在低维空间中表示此高维投影数据。可以通过一些众所周知的模式识别技术来执行尺寸减小。然后可以基于降维数据集构建分类器。一个流行的选择是高斯混合模型，其中每个打印机都可以用高斯分布表示。所有打印机的分布有助于我们确定来自未知打印机的投影的混合系数。最后，决策算法可以为正确的打印机投票。在本文中，我们将描述识别未知打印机的不同分类算法。我们将在我们的打印机银行中基于几种不同的EP打印机介绍实验。将比较基于不同分类器的分类结果。

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