Handwritten signature represents a person’ s identity. Although overall patterns among the signatures of same person
remain same, there can appear natural variations because two or more signatures of same person written within a moment and
keeping a sufficient time gap, cannot be exactly same. These natural variations result in intrapersonal variations. In the present
study, signature samples were collected from each participant under different situations of body position, paper texture, paper
position etc. to successfully capture the intrapersonal variations. Two features, namely area and height-width ratio (HWR) were
extracted for each signature using appropriate image processing techniques. These features were then modelled to the Single
Exponential Smoothing Time Series Technique as well as our developed methodology to predict the variations. Using this
technique the Positive Predictive Values (PPV) and False Rejection Rate (FRR) for both these features were found to be 88% ,
12% and 95.78%, 4.22% respectively.
Keywords
offline signature analysis, image processing, time series, single exponential smoothing technique, predictive modeling
SONGKLANAKARIN JOURNAL OF SCIENCE & TECHNOLOGY
Published by : Prince of Songkla University Contributions welcome at : http://rdo.psu.ac.th
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