Abstract
Hand signature is one of human characteristic that human have since birth, which can be used as identity recognition. A high accuracy signature recognition is needed to identify the right owner of signature. This study present signature identification using a combination method between Deep Learning and Euclidean Distance. 3 different signature datasets are used in this study which consist of SigComp2009, SigComp2011, and private dataset. Signature images preprocessed using binary image conversion, Region of Interest, and thinning. Several testing scenarios is applied to measure proposed method robustness, such as usage of various Pretrained Deep Learning, dataset augmentation, and dataset split ratio modifier. The best accuracy achieved is 99.44% with high precision rate.
Highlights
Signature is human identifier biometrics that is well known and recognized as a tool for identifying a person [1]
This study proposed an offline signature identification using combination methods between Pretrained Deep Learning and Euclidean Distance
Pretrained Deep Learning is used as feature extraction, while Euclidean Distance is used as an identification method
Summary
Signature is human identifier biometrics that is well known and recognized as a tool for identifying a person [1]. The signature was recognized as a biometric feature after UNCITRAL established the first digital signature law in the early 90s. Signature Recognition can be classified into two main groups, which consist of online signature and offline signature. Online signature recorded by using touch screens panels like smartphones or tablets. The recorded signature has its feature extracted, such as pressure points and the path or steps taken while creating the signature. Offline signature only needs scanning process on the signature image and remove the needed feature based on the scanned image [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Lontar Komputer : Jurnal Ilmiah Teknologi Informasi
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
