HYBRID COMPUTATIONAL INTELLIGENCE BASED MODEL FOR SIGNATURE VERIFICATION SYSTEM

Abstract

The Hybrid Computational Intelligence Based Model for Signature Verification System is an intelligent biometric authentication system designed to verify handwritten signatures using Artificial Intelligence and Machine Learning techniques. Traditional signature verification methods are often affected by forgery, manual errors, and lack of accuracy. The proposed system integrates hybrid computational intelligence techniques such as Neural Networks, Fuzzy Logic, and Image Processing algorithms to improve signature verification accuracy and reliability.
The system captures signature images, preprocesses them, extracts important features, and compares them with stored signature patterns using machine learning algorithms. The proposed model can identify genuine and forged signatures efficiently by analyzing signature structure, stroke patterns, pressure variations, and writing behavior.
The system provides secure authentication, reduces fraudulent activities, improves verification accuracy, and minimizes manual verification effort. The proposed solution can be used in banking systems, legal document verification, business authentication, and secure access control systems.