Abstract
AbstractOff-line signature verification is an important form of behavioral biometric identification. We present a method utilizing Modified Direction Feature(MDF) and Microstructure Feature(MSF) to tackle the problem. MDF and MSF belong to geometric structure features, but these two features are different from each other in each emphasis. In our study, global information in signatures’ boundaries is represented by MDF, while local information is represented by MSF. In order to get features with lower dimensions, principal component analysis is employed to reduce redundant dimensions. In addition, we adopt support vector machine as classifier for verification process. The proposed strategy is evaluated on the GPDS and MCYT corpora. Experimental results have demonstrated that the proposed method is effective to improve off-line signature verification accuracy.KeywordsOff-line Signature VerificationModified Direction FeatureMicrostructure FeatureCombinationSupport Vector Machine
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