VLSI based on efficient signature verification

an efficient offline signature verification
method based on an interval symbolic representation and a
fuzzy similarity measure is proposed. In the feature extraction
step, a set of local binary pattern-based features is computed
from both the signature image and its under-sampled bitmap.
Interval-valued symbolic data is then created for each feature in
every signature class. As a result, a signature model composed
of a set of interval values (corresponding to the number of
features) is obtained for each individual’s handwritten signature
class. A novel fuzzy similarity measure is further proposed to
compute the similarity between a test sample signature and the
corresponding interval-valued symbolic model for the verification
of the test sample. To evaluate the proposed verification approach,
a benchmark offline English signature data set (GPDS-300) and a
large data set (BHSig260) composed of Bangla and Hindi offline
signatures were used

verification