TraceNet: AI-Powered Missing Person detection System

The increasing number of missing person cases worldwide presents a significant challenge to law
enforcement agencies and communities. TraceNet is an advanced AI-powered Missing Person Identification
System designed to address this issue through the integration of machine learning, computer vision, and data
analytics. The system utilizes deep learning-based facial recognition models, particularly Convolutional
Neural Networks (CNNs), to analyze and match facial features from images and video streams against a
centralized database of missing individuals. TraceNet supports real-time identification by processing
surveillance footage, public camera feeds, and user-submitted images, enabling faster detection and response.
The system is capable of handling variations in lighting conditions, facial expressions, aging, and occlusions,
thereby improving accuracy and reliability. In addition, it incorporates geolocation tagging and alert
mechanisms to notify authorities when potential matches are detected. To ensure ethical deployment,
TraceNet emphasizes data privacy and security by implementing encryption, controlled access, and
compliance with data protection standards. The platform is designed to be scalable, user -friendly, and
accessible to authorized personnel, facilitating seamless collaboration between law enforcement agencies and
the public. Overall, TraceNet demonstrates how artificial intelligence can be effectively leveraged to enhance
public safety, streamline search operations, and significantly improve the chances of locating missing
individuals in a timely manner.