Signature identification through the use of deformable structures

Automatic signature verification is a well-established and active research area with numerous applications. In contrast, automatic signature identification has been given little attention, although there is a vast array of potential applications that could use the signature as an identification tool. This paper presents a novel approach to the problem of signature identification. We introduce the use of the revolving active deformable model as a powerful way of capturing the unique characteristics of the overall structure of a signature. Experimental evidence as well as intuition support the idea that the overall structure of a signature uniquely determines the signature in the majority of cases. Our revolving active deformable model originates from the snakes introduced in computer vision by Kass et al., but its implementation has been tailored to the task at hand. This computer-generated model interacts with the virtual gravity field created by the image gradient. Ideally, the uniqueness of this interaction mirrors the uniqueness of the signature's overall structure. The proposed method obviates the use of a computationally expensive segmentation approach and is parallelizable. The experiments performed with a signature database show that the proposed method is promising.