Identification of Genuine and Artificial Wood Grain Based on PCA-SVM

Abstract

This study presents an integrated method combining Principal Component Analysis (PCA) and Support Vector Machine (SVM) to distinguish genuine and artificial wood grains. The approach is based on acquiring nine-dimensional gloss data measured under varying angles and texture orientations, along with two surface roughness parameters: the arithmetic mean height (S_a) and maximum peak height (S_z). The dataset was divided into training and testing sets with a ratio of 7:3 after standardized. PCA was applied to the training set to extract the top k principal components. Then, these components served as input features for training an SVM classifier, whose discriminative performance was evaluated on the test set. Experimental results indicated that the proposed method achieved an accuracy of 96.76%, an F1-score of 0.9761, and a Matthews correlation coefficient (MCC) of 0.9285, substantially outperforming comparative models including standalone SVM, Logistic Regression (LR), Partial Least Squares (PLS), and Principal Component Regression (PCR). The method demonstrated high efficiency and robustness in distinguishing wood grain types, suggesting strong potential in practical engineering applications such as quality control and material authentication.