Non-destructive Recognition of Copy Paper Based on Advanced Spectral Fusion and Feature Optimization

ZHANG Zhen; WANG Ji-fen; LIU Jin-tong

doi:10.19969/j.fxcsxb.21051704

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Non-destructive Recognition of Copy Paper Based on Advanced Spectral Fusion and Feature Optimization

Experimental Techniques and Methods | 更新时间：2023-09-19

- Non-destructive Recognition of Copy Paper Based on Advanced Spectral Fusion and Feature Optimization
- Journal of Instrumental Analysis Vol. 42, Issue 9, Pages: 1181-1187(2023)
- 作者机构：
  
  中国人民公安大学侦查学院，北京 100038
- 作者简介：
- 基金信息：
- DOI：10.19969/j.fxcsxb.21051704
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张震,王继芬,刘津彤.基于先进光谱融合技术-特征优化的复印纸无损识别[J].分析测试学报,2023,42(09):1181-1187.

ZHANG Zhen,WANG Ji-fen,LIU Jin-tong.Non-destructive Recognition of Copy Paper Based on Advanced Spectral Fusion and Feature Optimization[J].Journal of Instrumental Analysis,2023,42(09):1181-1187.
张震,王继芬,刘津彤.基于先进光谱融合技术-特征优化的复印纸无损识别[J].分析测试学报,2023,42(09):1181-1187. DOI： 10.19969/j.fxcsxb.21051704.

ZHANG Zhen,WANG Ji-fen,LIU Jin-tong.Non-destructive Recognition of Copy Paper Based on Advanced Spectral Fusion and Feature Optimization[J].Journal of Instrumental Analysis,2023,42(09):1181-1187. DOI： 10.19969/j.fxcsxb.21051704.

摘要

为实现对犯罪现场复印纸的快速、无损、准确分类识别，该文将先进光谱融合技术应用于犯罪现场复印纸的“来源地-厂家-品牌”三维特征刻画与识别。采用显微共聚焦拉曼光谱和傅里叶变换红外光谱技术采集了4个来源地（山东、河南、陕西、江苏）共200个样本的光谱数据信息。选择并比较了希尔伯特变化和去卷积等预处理方法对模型区分能力的影响，借助主成分分析降维，并基于单一红外光谱数据集、拉曼光谱数据集和3种光谱融合数据集构建贝叶斯判别分类模型。通过对不同模型分类准确率的比较，选取了以全谱数据集为基础的初级融合作为复印纸三维特征分类的最佳模型，分别实现了来源地（96.0%）、厂家（100%）、品牌（92.3%）的准确区分，为其在法庭科学研究领域的应用提供了参考与借鉴。

Abstract

In order to accurately recognize the copy paper found at the crime scene，the“source-manufacturer-brand”three-dimensional characteristics details of them were described and recognized by advanced spectral fusion technology. Spectral data of 200 samples from four sources（Shandong，Henan，Shaanxi and Jiangsu） were collected by microscopic confocal Raman spectroscopy and Fourier transform infrared spectroscopy. The effects of pre-processing methods including Hilbert variation，deconvolution were selected and compared. Principal component analysis was considered to extract characteristic variables. The Bayesian discriminant classification model was built based on different datasets including the single infrared spectra dataset，Raman spectral dataset and three spectral fusion approach datasets. The primary fusion based on the full spectra dataset was selected as the optimal model for recognizing the three-dimensional characteristics details of copy paper by comparing the classification accuracy of different models. It realized the accurate classification of source（96.0%），manufacturer（100%） and brand（92.3%），respectively，which could provid some reference for its application in the field of forensic science.

关键词

先进光谱融合复印纸特征优化无损识别

Keywords

advanced spectral fusioncopy paperfeature optimizationnon-destructive recognition

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