Abstract: In traditional user authentication systems, identity verification is commonly based on textual inputs like usernames, passwords, or personal IDs, which are often vulnerable to misuse, forgetfulness, or theft. Existing systems that attempt face-based recognition either store static images or depend on manual matching, lacking automation and real-time detection capabilities. These approaches do not support live face capturing and fail to fetch dynamic user-related information from a database, resulting in inefficient or insecure identification mechanisms. The proposed system addresses these limitations by introducing a live face capturing and identification framework integrated with OpenCV. When a user registers, their face is captured in real-time using the system's web interface connected to the webcam through OpenCV. The captured image is then stored securely in the server’s file system or database. During login or verification, the system again uses OpenCV to capture a fresh face image and compares it with stored images using face similarity techniques. Upon successful matching, all relevant user information is dynamically fetched from the database and displayed securely on the interface. This system ensures that only the rightful user gains access to sensitive data, enhancing both security and user experience. The modular architecture seamlessly integrates live camera input via OpenCV, secure data storage, and efficient face comparison logic, offering an intelligent and real-time authentication mechanism suitable for modern web applications.

Downloads: | DOI: 10.17148/IARJSET.2026.13377