Main Article Content
A method for offline signature verification is presented in this paper. It is based on the segmentation of the
signature skeleton (through standard image skeletonization) into unambiguous sequences of points, or unambiguously connected skeleton segments corresponding to vectorial representations of signature portions. These segments are assumed to be the fundamental carriers of useful information for authenticity verification,
and are compactly encoded as sets of 9 scalars (4 sampled coordinates and 1 length measure). Thus signature authenticity is inferred through Euclidean distance based comparisons between pairs of such compact representations. The average performance of this method is evaluated through experiments with offline versions of signatures from the MCYT-100 database. For comparison purposes, three other approaches are applied to the same set of signatures, namely: (1) a straightforward approach based on Dynamic Time Warping distances between segments, (2) a published method by , also based on DTW, and (3) the average human performance under equivalent experimental protocol. Results suggest that if human performance is taken as a
goal for automatic verification, then we should discard signature shape details to approach this goal. Moreover, our best result – close to human performance – was obtained by the simplest strategy, where equal weights were given to segment shape and length.
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