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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 on 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.

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Research has been active in the field offorgeiy detection, but relatively little work has been done on the detection of skilled forgeries. In this paper, we present an algorithm for detecting skilled forgeries based on a local correspondence between a questioned signature and a model obtained a priori. Writer-dependent properties are measured at the substroke level and a cost function is trained for each writer. When a candidate signature is presented, the same features are extracted and matched against the model. We present a description of the features and experimental results.

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We present a novel ensemble architecture for learning problem-solving techniques from a very small number of expert solutions and demonstrate its effectiveness in a complex real-world domain. The key feature of our "Generalized Integrated Learning Architecture" (GILA) is a set of integrated learning and reasoning (ILR) components, coordinated by a central meta-reasoning executive (MRE). The ILRs are weakly coupled in the sense that all coordination happens through the MRE. Each ILR learns independently from a small number of expert demonstrations of a complex task. During the performance, each ILR proposes partial solutions to subproblems posed by the MRE, which are then selected from and pieced together by the MRE to produce a complete solution. We describe the application of this novel learning and problem solving architecture to the domain of airspace management, where multiple requests for the use of airspaces need to be deconflicted, reconciled and managed automatically. Formal evaluations show that our system performs as well as or better than humans after learning from the same training data. Furthermore, GILA outperforms any individual ILR run in isolation, thus demonstrating the power of the ensemble architecture for learning and problem solving.

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