Enhancing Access Control: A Biometric Approach with Facial Recognition and Near-Field Communication Integration
Rahulkrishnan Ravindran, Sabarinath Udayakumar, Yellanki Revanth, Kalakunnath Namitha · 2024
This study presents a human identity authentication framework that integrates facial recognition and biometric scanning, enhancing security and speeding up individual identification. The system also includes careful observation of people entering and leaving a designated area, keeping detailed logs in a MongoDB back-end. This strategic integration allows for effective tracking and retrieval of vital information about movement patterns within the monitored area. TensorFlow models, which use Convolutional Neural Networks(CNN), are the core of the identification process, contributing to unmatched accuracy in person recognition. This study provides a comprehensive investigation of the cooperative combination of bio-metrics, facial recognition, CNN, and backend database management for identity verification, raising the bar for their effectiveness, security, and user-friendliness in today’s technological environments. The CNN based algorithm was opted for making facial recognition a much more accurate and easier process.