Abstract: In order to improve the accuracy and efficiency of the identification of genuine and fake cigarette label paper, and reduce the experience requirements and subjectivity of identification, this paper proposes a method for identifying cigarette label paper based on feature point registration. Sample images were acquired based on a unified image acquisition standard, and discriminant predictors were acquired through feature point extraction and description based on SIFT algorithm, feature matching and image registration based on homograph transformation. Logistic regression algorithm and gradient boosting classification decision tree algorithm were selected to construct a binary classification model for training and evaluation. We performed experimental evaluation on 64 cigarette specifications and 2918 sample data sets. The results show that the accuracy of the proposed identification method is higher than 95%. The stability and effectiveness of the proposed identification method are verified through a comparative experiment.