Online slant identification algorithm using vector rules

• Published in: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
• Main Author: Yusof R.; Abdul Rahman S.; Yusoff M.; Mutalib S.; Mohamed A.
• Format: Conference paper
• Language: English
• Published: 2008
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-54249097804&doi=10.1007%2f978-3-540-69839-5_62&partnerID=40&md5=b5c9d2298c50960b7c9e9bb4ac9b775f

Signatures are among the most widely accepted personal attributes for identity verification. There are a lot of features that can be discovered in signature which are either dynamic or static features type. An algorithm needs to be designed to extract these signature features. Online system uses pressure sensitive tablets to capture signature of individual as they sign thus analysis can be done directly and immediately. This research explored slant feature algorithm since signature is usually slanted due to the mechanism of handwriting and the human personality. The proposed algorithm are used to formulate the Signature Extraction Features System (SEFS) which provides a set of tools that allow the users to extract slant features in signature automatically for analysis purposes. Twenty individuals from different background are randomly selected to have their signature taken. Their signatures are captured on a tablet and the SEFS would than gather and store the raw data. The image of the signature that is created by the SEFS would be used as samples for the questionnaire to identify the features of slant, where the questionnaires are given to human expert for evaluation. The results from the SEFS are compared with the result from the questionnaire. Results produced by the algorithm for slant extraction shows 85% identical answers compared to the outcome by human expert. These show that the algorithm proposed are promising for further exploration. © 2008 Springer-Verlag Berlin Heidelberg.