Signature is an important and useful behavioural biometric which exhibits significant amount of non- linear variability. In this study, the authors concentrate on finding an envelope shape feature known as 'chord ...
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Signature is an important and useful behavioural biometric which exhibits significant amount of non- linear variability. In this study, the authors concentrate on finding an envelope shape feature known as 'chord moments'. Central moments such as the variance, skewness and kurtosis along with the first moment (mean) are computed from sets of chord lengths and angles for each envelope reference point. The proposed chord moments adequately quantify the spatial inter-relationship among upper and lower envelope points. The moment-based approach significantly reduces the dimension of highly detailed chord sets and is experimentally found to be robust in handling non-linear variability from signature images. The proposed chord moments coupled with the support vector machine classifier lead to a writer dependent off-line signature verification system that achieves state-of-the-art performance on the noisy center of excellence for document analysis and recognition database.
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