Paper Fingerprint by Forming Fabric: A Univariate Feature Selection Approach Using Periodic Marks Analysis

Abstract

Evidence by which to confirm the location and approximate manufacturing date of document paper is a critical task in forensic investigations, particularly in cases involving suspected forgery or document manipulation. In this study, periodic marks formed during the papermaking process were analyzed using light-transmitted images captured by a two-dimensional lab formation sensor. Step and angle data from the top five intensity peaks were extracted and used to train tree-based classification models. To handle directional symmetry, a modulo 180° transformation was applied to the angle data. The random forest (RF) classifier outperformed decision tree (DT) and extreme gradient boosting (XGB) models, achieving the highest F1 score. Feature importance analysis revealed that the step and angle at the third intensity level were the most discriminative features, likely reflecting structural characteristics of forming fabrics or drainage patterns. A simplified univariate strategy using these features also showed potential for estimating production periods. However, differences between the top and bottom surfaces—particularly in twin-wire systems—introduced classification bias, highlighting the need to separately classify paper sides in forensic datasets. Overall, this study demonstrates the feasibility and limitations of using periodic mark analysis for document dating.