Forgery detection by local correspondence
Signatures may be stylish or unconventional and have many personal characteristics that are challenging to reproduce by anyone other than the original author. For this reason, signatures are used and accepted as proof of authorship or consent an personal checks, credit purchases and legal documents. Currently signatures are verified only informally in many environments, but the rapid development of computer technology has stimulated great interest in research on automated signature verification and forgery detection. In this paper, we focus on forgery detection of offline signatures. Although a great deal of work has been done on offline signature verification over the past two decades, the field is not as mature as online verification. Temporal information used in online verification is not available offline and the subtle details necessary for offline verification are embedded at the stroke level and are hard to recover robustly. We approach the offline problem by establishing a local correspondence between a model and a questioned signature. The questioned signature is segmented into consecutive stroke segments that are matched to the stroke segments of the model. The cost of the match is determined by comparing a set of geometric properties of the corresponding substrokes and computing a weighted sum of the property value differences. The least invariant features of the least invariant substrokes are given the biggest weights, thus emphasizing features that are highly writer-dependent. Random forgeries are detected when a good correspondence cannot be found, i.e. the process of making the correspondence yields a high cost. Many simple forgeries can also be identified in this way. The threshold for making these decisions is determined by a Gaussian statistical model. Using the local correspondence between the model and a questioned signature, we perform skilled forgery detection by examining the writer-dependent information embedded at the substroke level and try to capture unballistic motion and tremor information in each stroke segment, rather than as global statistics. Experiments on random, simple and skilled forgery detection are presented.
forgery detection random forgery simple forgery skilled forgery