E-Authentication System using Eye-Blinking and Deep Learning

The Personal Identification Number (PIN) are commonly employed as a security measure for authentication purposes. When utilizing a PIN for verification of authentication, the users are required to input a physical PIN. However, this method may be susceptive to security vulnerabilities through methods such as observing someone’s shoulder or tracking thermal signatures. In this research, we adopted Eye-Blinking for authentication of the system, with the advantage that it leaves no bodily footprints in the system and provides a more robust alternative for advanced security. The eye-blinking pattern is a highly secured and unique biometric identifier that cannot be easy replicates, making it difficult to hack or bypass. An E-Authentication System using Eye-Blinking and its analysis using deep learning explains an individual’s eye-blinking patterns as a unique identifier to verify their identities. The systems capture their eye-blinking patterns and compare them to the stored patterns in its databases. If the patterns match, the systems grant access to the user. The systems aim at achieving high accuracy, reliability, and integration with existing security infrastructure and authentication protocols.