Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)

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Signatures Verification using CNN and HOG including Voting Classifier

Authors
B. Venkata Sivaiah1, *, D. Vyshnavi2, B. Mamatha2, M. Harish2, A. Sathish Kumar2, N. Siva3, Ashok Patel4
1Assistant Professor, Dept of CSE (DS), Mohan Babu University, Tirupati, India
2UG Scholar, Department of CSSE, Sree Vidyanikethan Engineering College, Tirupati, India
3Assistant Professor, Department of CSE, Siddharth Institute of Engineering and Technology (Autonomous), Puttur, AP, India
4University of Massachusetts Dartmouth, Hanover, USA
*Corresponding author. Email: siva.bheem@hotmail.com
Corresponding Author
B. Venkata Sivaiah
Available Online 30 July 2024.
DOI
10.2991/978-94-6463-471-6_58How to use a DOI?
Keywords
Manual signature authentication systems; CNN; HOG; Deep learning
Abstract

This study suggests a unique hybrid feature extraction technique that expands the possibilities of Manual signature authentication systems. This method efficiently finds important characteristics in signature photos by combining Convolutional Neural Network (CNN) and Histogram of Oriented Gradients (HOG) approaches with a Decision Tree-based feature selection algorithm. Three classifiers were used in the evaluation: Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Long Short-Term Memory (LSTM). All three classifiers showed excellent accuracy in differentiating between genuine and fake signatures. Furthermore, a Voting Classifier (RF + DT) in the feature extraction process lead to an unparalleled 100% accuracy on testing datasets. This novel hybrid technique not only outperforms the findings of the original research but also demonstrates the resilience and adaptability of the suggested methodology, resulting in notable advancements in the performance of Manual signature authentication systems, especially against proficient forgeries.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
Series
Advances in Computer Science Research
Publication Date
30 July 2024
ISBN
10.2991/978-94-6463-471-6_58
ISSN
2352-538X
DOI
10.2991/978-94-6463-471-6_58How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - B. Venkata Sivaiah
AU  - D. Vyshnavi
AU  - B. Mamatha
AU  - M. Harish
AU  - A. Sathish Kumar
AU  - N. Siva
AU  - Ashok Patel
PY  - 2024
DA  - 2024/07/30
TI  - Signatures Verification using CNN and HOG including Voting Classifier
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 598
EP  - 608
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_58
DO  - 10.2991/978-94-6463-471-6_58
ID  - Sivaiah2024
ER  -