Handwritten Signature Identification Based on Hybrid Features and Machine Learning Algorithms

Hashim, Zainab; Mohsin, Hanaa; Alkhayyat, Ahmed

doi:10.33103/uot.ijccce.24.1.4

	Handwritten Signature Identification Based on Hybrid Features and Machine Learning Algorithms
IRAQI JOURNAL OF COMPUTERS, COMMUNICATIONS, CONTROL AND SYSTEMS ENGINEERING
Volume 24, Issue 1, March 2024, Pages 43-56 PDF (1.03 M)
Document Type: Research Paper
DOI: 10.33103/uot.ijccce.24.1.4
Authors
Zainab Hashim^* ¹; Hanaa Mohsin¹; Ahmed Alkhayyat²
¹Department of Computer Sciences, University of Technology, Baghdad, Iraq
²Qaultiy Assurance Department, The Islamic University, Najaf, Iraq
Abstract
Handwritten signature identification is a process that determines an individual’s true identity by analyzing their signature. This is an important task in various applications such as financial transactions, legal document verification, and biometric systems. Various techniques have been developed for signature identification, including feature-based methods and machine learning-based methods. However, verifying handwritten signatures in digital transactions and remote document authentication is still challenging. The inherent variety in people’s signatures, which may occur due to factors such as mood, exhaustion, or even the writing tool used, contributes to the problem. Furthermore, the proliferation of sophisticated forgery methods, such as freehand mimicking and sophisticated picture manipulation, necessitates the development of reliable and precise tools for identifying authentic signatures from fake ones.The present paper suggests a method for identifying signatures based on integrating static (off-line) handwritten signature data. This is done by fusing three types of signature features: Linear Discriminant Analysis (LDA) as appearance-based features, Fast Fourier Transform (FFT) as frequency- features, and Gray-Level Co-occurrence Matrix (GLCM) as texture-features. Then, these fused features are inputted into four types of machine learning algorithms: Naive Bayes, K-Nearest Neighbor, Decision Tree, and AdaBoost classifiers, to identify each person and to find the most robust algorithm in terms of accuracy and precision and recall. For experiments, we have used two famous datasets: SigComp2011 and CEDAR. After training datasets, the highest accuracy achieved was 100% on the CEDAR dataset and 94.43% on the SigComp2011 dataset using a Naive Bayes classifier.
Keywords
Fast Fourier Transform; Gray-Level Co-occurrence Matrix; Handwritten Signature; Linear Discriminant Analysis; Machine Learning

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