Open AccessRecognition of person’s character through the shape of signature using Radial Basis Function Neural Network (RBFNN) method
Author(s) -
Faisol Faisol,
Zainal Arifin Ahmad,
Halumatus Sakdiyah,
Qurrotul Aini,
Kuzairi,
Tony Yulianto
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1538/1/012059
Subject(s) - digital signature , signature (topology) , entropy (arrow of time) , pattern recognition (psychology) , artificial neural network , matlab , artificial intelligence , computer science , software , image (mathematics) , algorithm , mathematics , physics , computer security , hash function , geometry , quantum mechanics , programming language , operating system
Signatures are a marker or identity that exists in a document. Signatures have an important role in verifying and legalizing documents. The purpose of this study is to apply image processing techniques to signatures and to identify patterns of signature images based on entropy values. The stages of the research include taking respondents’ data in the form of analog image signatures, then the acquisition of digital signature images by scanning the signatures. The next step is to convert digital hand images from true color to binary. The last step is calculating the entropy value, recording the entropy value calculation time using Matlab software and looking at the distribution of entropy values from each signature image. From this method, the result of 50 data of signatures taken from students of the Islamic University of Madura (UIM) of the FMIPA, there are 11 to 1 st type, 1 to 2 nd type, 13 to 3 th type, 4 to 4 th type, 9 to 5 th type, 3 to 8 th type, 7 to 9 th type and 2 to 11 th type.