Identifikasi Tanda Tangan Online Menggunakan Metode Ekstraksi Fitur Bentuk Global dan Lokal
DOI:
https://doi.org/10.30865/resolusi.v4i2.1481Keywords:
Online Signatures; Global Form Features; Local Form Features; Identification; Number of Segments; Segment DirectionAbstract
Online signatures have become one part of biometrics that is widely used to identify a person, because each person has a different signature and each signature has unique physiological characteristics. The main problem in identifying online signatures is the inconsistency in the form of a person's signature when writing a signature on a surface. Special analysis is needed regarding the main characteristics that can be used to anticipate these inconsistencies so that a person's signature can be identified correctly. This research aims to obtain features that can be used to facilitate the process of identifying signature owners. This research uses a method based on global shape features and local shape features. Global shape features are used to capture signature candidates identified as having the same or similar global form. After that, local shape features are used to measure the detailed shape similarity of each signature candidate. The trial was carried out using primary data totaling 60 signatures collected from 12 respondents. Each respondent provides five signatures which are divided into three signatures as training data and two signatures as test data. The test results of the proposed method and algorithm show an identification accuracy rate of 96.67%. This shows that the extraction method from global shape features and local shape features can be used for the development of an online biometric signature recognition system.
Downloads
References
V. Iranmanesh, S. M. S. Ahmad, W. A. W. Adnan, S. Yussof, O. A. Arigbabu, and F. L. Malallah, “Online Handwritten Signature Verification Using Neural Network Classifier Based on Principal Component Analysis,” Sci. World J., vol. 2014, 2014, doi: 10.1155/2014/381469.
T. Fotak, M. Ba?a, and P. Koruga, “Handwritten Signature Identification Using Basic Concepts of Graph Theory,” WSEAS Trans. Signal Process., vol. 7, no. 4, pp. 145–157, 2011.
R. Jana, R. Saha, and D. Datta, “Offline Signature Verification using Euclidian Distance,” Int. J. Comput. Sci. Inf. Technol., vol. 5, no. 1, pp. 707–710, 2014, [Online]. Available: http://www.ijcsit.com/docs/Volume 5/vol5issue01/ijcsit20140501153.pdf
S. Pal, U. Pal, and M. Blumenstein, “Signature-Based Biometric Authentication,” Comput. Intell. Digit. Forensics Forensic Investig. Appl., 2014, doi: https://link.springer.com/chapter/10.1007/978-3-319-05885-6_13.
K. P. Nagbhidkar and P. V. Bagdi, “Online Signature Verification on Smart Phone Using Discrete Wavelet Transforms,” IORD J. Sci. Technol., vol. 2, no. 2, pp. 1–6, 2015.
D. Impedovo and G. Pirlo, “Automatic signature verification: The state of the art,” IEEE Trans. Syst. Man Cybern. Part C Appl. Rev., vol. 38, no. 5, pp. 609–635, 2008, doi: 10.1109/TSMCC.2008.923866.
M. Faundez-Zanuy, M. Diaz, and M. A. Ferrer, “Online Signature Recognition: A Biologically Inspired Feature Vector Splitting Approach,” Cognit. Comput., 2023, doi: 10.1007/s12559-023-10205-9.
M. Saleem and B. Kovari, “Online signature verification using signature down-sampling and signer-dependent sampling frequency,” Neural Comput. Appl., vol. 0123456789, 2021, doi: 10.1007/s00521-021-06536-z.
S. Castan, J. Zhao, and J. Shen, “Optimal Filter for Edge Detection Methods and Results,” Comput. Vis. - ECCV 90 First Eur. Conf. Comput. Vision. Antibes, Fr. April 23-27, 1990. Proc., pp. 13–17, 1990, doi: 10.1007/BFb0014845.
J. Canny, “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., no. Desember, 1986, doi: 10.1109/TPAMI.1986.4767851.
R. Deriche, “Using Canny ’ s Criteria to Derive a Recursively Implemented Optimal Edge Detector,” Int. J. Comput. Vis., pp. 167–187, 1987.
S. Madenda, R. Missaoui, M. Paindavoine, and J. Vaillancourt, “An Enhanced Detector of Blurred and Noisy Edges,” Signal Process. Image Enhanc. Multimed. Process. Springer, Boston, MA, pp. 127–140, 2014, doi: 10.1007/978-0-387-72500-0.
E. Y. Hidayat and M. F. Radiffananda, “Pengenalan Tanda Tangan Menggunakan Learning Vector Quantization dan Ekstraksi Fitur Local Binary Pattern,” CogITo Smart J., vol. 5, no. 2, pp. 123–136, 2019, doi: 10.31154/cogito.v5i2.180.123-136.
P. Gouton, Hayet Laggoune, R. K. Kouassi, and Michel Paindavoine, ”Ridge-line optimal detector”, Optical Engineering, vol. 39, no. 6, pp. 1602-1611, 2000.
Widodo, Suryarini. “Segmentation Handwriting Character On-line Using Characteristics Changes of Coordinate Y Value”, Disertasion, Gunadarma University. 2009.
Munggaran, Lulu Chairani. “Development of Handwriting Recognition and Evaluation Algorithms to Measure Child's Writing Skills”, Disertasion, Gunadarma University. 2009.
Acharya Tinku, Ray Ajoy K. “Image Processing, Principle and Application”. New Jersey: John Wiley & Sons, Inc. 2005.
Bahlmann, Claus. “Advanced Sequence Classification Techniques Applied to Online Handwriting Recognition”. Dissertasion. University of Freiburg, Germany. 2004.
Shamsuddin, M. & Mohamed, A. (2008), ‘Online signature slant feature identification algorithm’, WSEAS Transactions on Computer Research 3, 121–130. S
B.C. Octariadi and Y. Brianorman, "Pengenalan Pola Tanda Tangan Menggunakan Metode Jaringan Syaraf Tiruan Backpropagation", Jurnal Teknoinfo, Vol 14 No. 1, pp. 15-21 , 2020.
Bila bermanfaat silahkan share artikel ini
Berikan Komentar Anda terhadap artikel Identifikasi Tanda Tangan Online Menggunakan Metode Ekstraksi Fitur Bentuk Global dan Lokal
ARTICLE HISTORY
Issue
Section
Copyright (c) 2023 Cahyawati Diah Kusumarini, Fitrianingsih, Betty Suswati
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (Refer to The Effect of Open Access).
