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.