Circumferential Local Ternary Pattern: New and Efficient Feature Descriptors for Anti-Counterfeiting Pattern Identification

Abstract

An important aspect of querying whether a product is likely to be forged is to identify its anti-counterfeiting label. However, the use of image processing technology for label-specific texture analysis to quickly and effectively identify the anti-counterfeiting label has been widely studied. Aiming at the defects of the local binary pattern (LBP) and its variants in texture identification, this paper proposes a new texture model for anti-counterfeiting identification, that is, the circular local ternary pattern (CLTP). The highlight of our technology is that it extracts the effective local texture descriptors by using the random features of inkjet printing. This allows for the technology to not only resist the interference of noise and illumination in images of anti-counterfeiting patterns but also to encode and reorganize the fine linear shape structure. Specifically, this paper extracts the CLTP texture feature in the corresponding key areas and forms the final feature histogram vector for comparison through the one-to-one correspondence between the sample image and the inspected image of anti-counterfeiting pattern. Experiments prove that our method not only has high discrimination, stability and effectiveness but also provides a convenient and practical idea for anti-counterfeiting technology.