EOG-based Blink Authentication

Advanced identity authentication methods that offer ease and security have been created as a result of the internet’s widespread use and rising popularity of electronic gadgets. People are becoming increasingly dependent on these devices, though, which increases the potential for spoofing and privacy violations. The static photographs of faces or fingerprints used in many current authentication systems make them susceptible to attacks utilizing fabricated data. To solve this problem, this study offers a unique Dynamic Biometric Authentication System (DBAS) that integrates individual eye blink patterns with face authentication to increase system redundancy and security. The suggested system identifies the user using a variety of facial recognition techniques and then uses a unique blink pattern to confirm the user’s identification. In this study, an authentication system using eye-blinking Electro Oculo-gram (EOG) signals for safe person identification in response to the flaws in current authentication techniques is proposed. This system records blink signals using an Arduino-based EOG detection device and then extracts pertinent information from the data. In order to authenticate the participants, these traits are combined with a linear discriminant analysis (LDA) classifier.