Hostel Security

Hostel security is a growing concern for educational institutions, and traditional surveillance methods often fall short in ensuring quick responses to unauthorized access. This project proposes an innovative face detection system using Python that aims to solve this issue. The system employs OpenCV for real-time facial recognition and integrates Twilio for SMS notifications to alert the warden immediately if an unauthorized person enters the premises. It also triggers a sound alert to draw attention in case of a day scholar or unknown person being detected. The face detection process begins with capturing video input and processing each frame to detect faces. The system is connected to a pre-existing database of hostellers’ facial data. If a detected face matches an entry in the database, the system classifies it as a resident and displays their details. If no match is found, the system classifies the person as an unauthorized guest, triggers an alarm, and sends an SMS notification via Twilio to the hostel warden. This system allows hostel management to streamline monitoring, eliminate the need for constant manual checks, and react promptly to unauthorized access. It also provides an added layer of security by storing each detected person’s details, including the time of entry and their identity. A simple and user-friendly interface shows the current status, including green (hosteller) or red (unauthorized person). The primary objective of this system is to enhance hostel security by automating reducing the human monitoring process, intervention, and providing real-time alerts for immediate action. The results indicate high accuracy in face detection, fast classification, and efficient response time in alerting authorities. Overall, this solution offers an effective and scalable approach to improving hostel security and ensuring a safer environment for all residents.