Dynamic Keystroke And Biometric User Authentication

Abstract

Traditional Authentication Mechanisms Such As Passwords And PINs Are Widely Used For System Security; However, They Remain Vulnerable To Attacks Such As Password Theft, Shoulder Surfing, And Replay Attacks. To Address These Limitations, This Research Proposes A Dynamic Multi-modal Biometric Authentication System That Integrates Keystroke Dynamics And Face Recognition Using A Support Vector Machine (SVM) Classifier. The Proposed System Continuously Verifies User Identity By Analyzing Typing Behaviour And Facial Features Captured Through A Webcam. Keystroke Timing Characteristics Such As Dwell Time And Flight Time Are Extracted, While Facial Features Are Obtained Using Computer Vision-based Face Recognition Techniques. The Proposed Model Demonstrates Strong Potential For Applications Requiring Continuous And Secure Authentication Such As Online Banking And Financial Platforms.

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