Detecting off-line signature model using wide and narrow variety class of local feature

• Main Authors: Sediyono, Agung, Syamsu, YaniNur
• Format: Conference or Workshop Item
• Language: English
• Published: 2013
• Subjects: QA76 Computer software
• Online Access: http://repo.uum.edu.my/11975/1/PID77.pdf; http://repo.uum.edu.my/11975/; http://www.icoci.cms.net.my/proceedings/2013/TOC.html

There are so many questioned document cases in Indonesia, mostly related to disputed signatures, both are forgery and denial of the offline signature.The Indonesian forensic document examiners have been examining the signatures manually and they have not been implementing the computer in signatures identification optimally yet.Therefore, it needs help of computer based detection to speed up and support decision making in examining signature forgery.Many research in this field was done, but it still an open research especially in detection accuracy. Usually every detection method only dictates for certain class of forgery and uses only one phase detection.Otherwise, this research proposes two phase detection that has capability for detecting all classes of forgery.This approaches based on hypothesize that the detection of skilled signatures forgery can be identified using a wide variety of segments and random to moderate signature forgery can be identified using a narrow variation of segments.Otherwise, the skilled forgery will be detected using wide variety of local features.For future work, it has to be selected the appropriate segmentation technique to determine the narrow and wide variety area of signature and formula to calculate the distance among signatures.