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An efficient approach for dynamic signature recognition

Online signature recognition is an accepted biometric technique, because it is less expensive than other biometric techniques. In this paper, an efficient two-stage online signature recognition approach is presented. This approach depends on the initial analyses of global features using Euclidean distance to quickly discard outlier signatures; then followed by local features analysis using an enhanced DTW algorithm. An emphasis was created to extract stroke-associated features for global recognition phase as well as for signal pre-processing prior to local recognition. Stroke-related features used in the proposed system contribute well in enhancing the run-time performance by quick discriminating genuine and forgery signatures. Experiments are run out on MCYT-100 benchmark database. The proposed system is tested with 100 users - including 25 skilled forgery signatures and 25 genuine signatures per each user. The worst FAR recorded value is less than 4%, FRR is less than 20% and EER is less than 5%.