Another branch of biometrics, palmprint recognition is viewed as the most practical recognition procedure attributable to its benefits, i.e. minimal effort, ease of use and high accuracy. The originality of the proposed approach is based on a new ROI extraction and two-stage feature extraction method to extract principal lines from the ROI using multiscale Frangi vessel enhancement filtering followed by the new 2-component partition method that divides the principal line image into similar and non- similar images. Depending on the similarity and non-similarity score the palm image is classified. The point of this method is to design a proficient confirmation framework utilizing texture feature for palmprint recognition with lessened computational complexity and subsequently to build the matching pace and exactness. The experimental outcomes demonstrate that the classification accuracy of the proposed method is comparable to past elite techniques used for palmprint recognition. © 2018 IEEE.