Quantitative Statistics and Identification of Tool‐Marks

This study was designed to establish a feature identification method of tool‐mark 2D data. A uniform local binary pattern histogram operator was developed to extract the tool‐mark features, and the random forest algorithm was adopted to identify these. The presented method was used to conduct five groups of experiments with a 2D dataset of known matched and nonmatched tool‐marks made by bolt clippers, cutting pliers, and screwdrivers. The experimental results show that the proposed method achieved a high rate of identification of the tool‐mark samples generated under identical conditions. The proposed method effectively overcomes the disadvantage of unstable illumination of 2D tool‐mark image data and avoids the difficulty in mark inspection caused by manually preset parameters in the existing methods, thus reducing the uncertainty of inspected results.