濃度こう配特徴を用いたオンライン署名照合の高精度化

概要

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This paper proposes a new on-line signature verification technique which employs gradient features and a pooled within-covariance matrix of training samples not only of the user but also of the others. Gradient features are extracted from a signature image reflecting the velocity of pen movement as the grayscale so that both on-line and off-line features are exploited. All training samples of different signatures collected in design stage are pooled together with the users samples and used for learning within-individual variation matrix to reduce required sample size of the user to minimum number. The result of evaluation test shows that the proposed technique improves the verification accuracy by 4.9% when users sample of size three is pooled with samples with others. This result shows that the samples of different signatures are useful for training within-individual variation of a specific user.
社団法人電気学会の論文
2010-12-01