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Prediction of laser printers and cartridges based on three-dimensional profiles via discrimination analysis.
Jiang, Zi-Feng; Zhang, Qing-Hua; Wang, Ya-Chen; Liu, Yan-Ling; Zhao, Ya-Wen; Hao, Yu-Yu; Xu, Jing-Yuan; Yang, Xu; Chen, Xiao-Hong.
Affiliation
  • Jiang ZF; East China University of Political Science and Law, 1575, Wanhangdu Road, Shanghai 200042, PR China.
  • Zhang QH; Academy of Forensic Science, 1347, West Guangfu Road, Shanghai 200063, PR China.
  • Wang YC; Academy of Forensic Science, 1347, West Guangfu Road, Shanghai 200063, PR China.
  • Liu YL; East China University of Political Science and Law, 1575, Wanhangdu Road, Shanghai 200042, PR China.
  • Zhao YW; East China University of Political Science and Law, 1575, Wanhangdu Road, Shanghai 200042, PR China.
  • Hao YY; East China University of Political Science and Law, 1575, Wanhangdu Road, Shanghai 200042, PR China.
  • Xu JY; East China University of Political Science and Law, 1575, Wanhangdu Road, Shanghai 200042, PR China.
  • Yang X; Academy of Forensic Science, 1347, West Guangfu Road, Shanghai 200063, PR China. Electronic address: yangx@ssfjd.cn.
  • Chen XH; Academy of Forensic Science, 1347, West Guangfu Road, Shanghai 200063, PR China. Electronic address: chenxh@ssfjd.cn.
Forensic Sci Int ; 363: 112186, 2024 Aug 06.
Article in En | MEDLINE | ID: mdl-39127023
ABSTRACT
Printer source prediction is an important task when examining questioned documents. While some research has provided methods to predict the source printer of documents, with the advent of compatible consumables, printer prediction could become more complex and difficult. Predicting the source printer after replacing cartridges and identifying the source of printer cartridges are unresolved issues that are rarely addressed in current research. Herein, we introduce a novel technique to predict the manufacturer, model, and cartridges of laser printers (i.e., compatible, and original cartridges) used to produce a given document. Document samples produced using eight laser printers and 247 cartridges were collected to establish a dataset. Common manufacturers included HP, Canon, Lenovo, and Epson. After obtaining white-light images and three-dimensional profile images of printed characters, a morphological analysis was conducted by questioned document examiners (QDEs) using microscopy. Microscopic image features across a series of images were also extracted and analyzed using algorithms. Then, six high-dimensional reduction algorithms were used to obtain between- and within-printer variations as well as between- and within-cartridge variations. Finally, we conducted principal component analysis (PCA) and discriminant analysis. For 40 % of the samples, mixed discrimination analysis (MDA) and fixed discrimination analysis (FDA) were employed to predict the manufacturer, model and cartridge of laser printers used to produce the questioned printed document; the remaining 60 % samples comprised the training dataset. In the prediction of manufacturer, model and cartridge, our method achieved mean accuracies of 95.5 %, 97.5 %, and 90.2 %, respectively. Hence, this technique could reasonably aid in predicting the manufacturer, model, and cartridge of a laser printer, even if different cartridges are loaded into printers.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Forensic Sci Int Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Forensic Sci Int Year: 2024 Document type: Article