Distance-Level Fusion Strategies for Online Signature Verification

In this paper an approach for combining online signature authentication experts will be proposed. The different experts are based on one feature extraction method presented in our earlier work, the Biometric Hash algorithm [C. Viehauer, et al., (2002)], to which different distance measurement functions are applied. We will show that by the fusion of several algorithms with an appropriately parameterized strategy an improvement of the recognition accuracy can be achieved. The best fusion strategy results in a decrease of the EER of 12.1% in comparison to the best individual algorithm. The database we used contains 1761 genuine enrollments (with 4 signatures per enrollment), 1101 genuine verification signatures and 431 well skilled forgeries (so-called "brute force attack") by 22 persons. Based on our experimental results, we further discuss usability of alternative handwriting semantics such as pass phrases or PIN