Laser Printing Files Detection Method Based on Double Features

Abstract

A novel laser printing files detection method is proposed in this paper to solve the problem of low efficiency and difficulty in traditional detection. The new method is based on improved scale-invariant feature transform (SIFT) feature and histogram feature. Firstly, analyze the graphical features of different laser printing files. Different files have different printing texture features in valid data area. So segment the valid data area to remove the interference of background. Secondly, extract the histogram feature of the same character in the printing file. Normalize the histogram and then calculate the Bhattacharyya coefficient between the detected file and the original file to determine whether the detected file is right or fake. At the same time, calculate the SIFT features and match the detected file and the original file. To focus on the letter or character region, the SIFT features which are out of contour are deleted. Finally, the results of the two different methods are both used as the result of the identification. When any of the result is fake, the end result will be fake. In the self-built database experiment, in different printing files from different printers, the inkjet areas possess different image features. When scanning different files using 600 dpi, the detect accuracy is higher than 97%. This method was able to meet the reliability requirements of law.