FACE BASED AGE ESTIMATION FOR AUTOMATED PARENTAL CONTROL IN SOCIAL MEDIA

Authors:

P. Sindhu Reddy, DR.B.Ramesh, K.Gnana Prasuna, CH.Meghana, M.Poojitha, Lokesh vyas

ABSTRACT:

The rapid growth of digital technologies has significantly increased children's exposure to online content, raising serious concerns about their safety and psychological well-being. Traditional parental control systems, including password protection, restricted browsing modes, and monitoring tools, are often ineffective as they rely heavily on manual supervision and can be easily bypassed. To address these limitations, this study proposes an AI-driven parental safety system that leverages real-time facial age estimation to automatically regulate web content access.

The system is implemented as a browser extension that captures facial images using the device camera and processes them locally through deep learning models to estimate the user's age. A unique feature of the proposed system is the integration of a screen brightening mechanism, which enhances facial visibility under low-light conditions, thereby improving prediction accuracy. Based on the detected age, the system dynamically filters and restricts access to inappropriate or sensitive content while allowing safe and educational browsing.

Unlike existing solutions, this system ensures complete privacy by avoiding cloud storage and immediately deleting captured images after processing. The proposed model demonstrates improved accuracy, faster response time, and higher reliability in real-world scenarios compared to traditional parental control methods. This research contributes a scalable, privacy-preserving, and intelligent framework for enhancing online child safety across both desktop and mobile platforms