The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON COMPUTERS
Transactions ID Number: 29-257
Full Name: Azlinah Mohamed
Position: Associate Professor
Age: ON
Sex: Female
Address: FTMSK, UiTM Shah Alam
Country: MALAYSIA
Tel: 603-55211242
Tel prefix:
Fax: 603-55435501
E-mail address: azlinah@tmsk.uitm.edu.my
Other E-mails:
Title of the Paper: Online Slant Signature Algorithm Analysis
Authors as they appear in the Paper: Azlinah Mohamed, Rohayu Yusof, Sofianita Mutalib, Shuzlina Abdul Rahman
Email addresses of all the authors: azlinah@tmsk.uitm.edu.my, sofi@tmsk.uitm.edu.my, shuzlina@tmsk.uitm.edu.my
Number of paper pages: 10
Abstract: A vector rule-based approach and analysis to on-line slant signature recognition algorithm is presented. Extracting features in signature is an intense area due to complex human behavior, which is developed through repetition. Features such as direction, slant, baseline, pressure, speed and numbers of pen ups and downs are some of the dynamic information signature that can be extracted from an online method. This paper presents the variables involve in designing the algorithm for extracting the slant feature. Signature Extraction Features System (SEFS) is used to extract the slant features in signature automatically for analysis purposes. The system uses both local and global slant characteristics in extracting the feature. Local slant is the longest slant among the detected slant while the global slant represents the highest quantity of classified slant whether the slant are leftward, upright or rightward. Development and analysis are reported on a database compr!
ises of 20 signatures from 20 subjects. The system is compared to human expert evaluation. The results demonstrate a competitive performance with 85% accuracy.
Keywords: Slant feature, Online signature, Signature recognition, Signature analysis, Dynamic signature
EXTENSION of the file: .doc
Special (Invited) Session: Slant Algorithm for Online Signature Recognition
Organizer of the Session: 699-175
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