VERIFICATION OF STATIC SIGNATURE USING CONVOLUTIONAL NEURAL NETWORK
Creators
- 1. Candidate of Technical Sciences, Senior Lecturer of Software Engineering Department, Fergana Branch of Tashkent University of Information Technologies named after Muhammad al-Khorazmi
Description
This article is devoted to the development of a method that provides verification of handwritten signatures based on real samples obtained by scanning with a resolution of 800 dpi. Handwritten signature remains one of the most common identification methods and consideration of the problems of this promising area contributes to the search for a solution to this problem
One of the main stages of recognition is classification. This article describes the results of handwritten signature recognition using a convolutional neural network. A database of handwritten signatures of 10 people was used for experiments. The signatures are digitized as color images with a resolution of 850×550 pixels. There are 10 genuine and 10 fake signatures for each person. Experiments were carried out with the reduction of signatures to the size 128×128, 256×256, 512×512 pixels.
As a result of the study of this model, it has shown its effectiveness and practical suitability for use in biometric identification systems.
Files
13_161_70-74 Axundjanov U-.pdf
Files
(1.1 MB)
Name | Size | Download all |
---|---|---|
md5:1926f97ee099334c52f93d38b7e7afbf
|
1.1 MB | Preview Download |
Additional details
References
- Starovoitov V.V. Processing of iris images for recognition systems. Minsk: LAP LAMBERT Academic Publishing, 2018. - 188 p.
- Bobovkin M. V., Ruchkin V. A., Protkin A. A. Actual problems of theory and practice of forensic signature research // Vestnik of the Moscow University of the Ministry of Internal Affairs of Russia. - 2017. - Vol. 2. - P. 109-115.
- Hafemann L. G., Sabourin R., Oliveira L. S. Offline handwritten signature verification—literature review // 2017-th international conference on image processing theory, tools and applications. – IEEE, 2017. – P. 1-8.
- Starovoitov V. V., Golub Y. I. Comparative study of quality estimation of binary classification. Informatics. – 2020. – Vol. 17, iss. 1. – P. 87−101 (in Russian).
- Israfilov H. С. Investigation of image binarization methods // Science and Education Bulletin. - 2017. - Vol. 2. iss. 6 (30). - P. 43-50.
- Yankovskiy A. A., Bugrii A. N. Criteria for selecting a binarization method for the processing of images of laboratory analyses // Automated control systems and automatic devices. control systems and automation devices. - 2010. - Vol. 153. - P. 53-56.
- Choi S. S. et al. A survey of binary similarity and distance measures // Journal of systemics, cybernetics and informatics. – 2010. – Vol. 8., iss. 1. – P. 43-48.
- Canbek G. et al. Binary classification performance measures/metrics: A comprehensive visualized roadmap to gain new insights // 2017 International Conference on Computer Science and Engineering. – IEEE, 2017. – P. 821-826.
- Sokolova M., Lapalme G. A systematic analysis of performance measures for classification tasks // Information processing & management. – 2009. – Vol. 45., iss. 4. – P. 427-437.