Get to know the projects under the system and application category 🖥️
Embedded Files
📌 Project Abstract
Face Recognition Surveillance System
Face Recognition Surveillance System
Face Recognition Surveillance System
This project presents the design, implementation, and evaluation of Nexus, a face-recognition surveillance system that leverages technologies to enhance security operations across various environments. The core of Nexus lies in its integration of several machine learning algorithms, including YOLO for object detection, Retina Face for face detection, Face Net for facial recognition, and Deep Sort for person tracking. These technologies are combined to provide a robust system capable of real-time monitoring and accurate identity verification, even under challenging conditions. The implementation of Nexus is described in detail, emphasizing its modular design which ensures scalability and ease of use. This project also discusses the practical implications of deploying Nexus in real-world environments, showcasing its effectiveness in enhancing surveillance capabilities and proactive security measures.
💻 Project Overview
Objectives
Objectives
Develop a real-time surveillance system utilizing face recognition technology.
Enhance the system through accurate alerting and efficient data monitoring.
Integrate machine learning models for object detection, tracking and face recognition.
Provide user-friendly interfaces for remote system management accompanied with 2-factor authentication .
Project strengths
Project strengths
The system supports real-time video streaming and face recognition.
Utilizes state-of-the-art models like YOLO for object detection, retina face for face detection, and InceptionResnetV1 for face recognition.
Designed to handle multiple streams and large-scale deployment.
Enhanced security features, including 2-factor authentication, remote system management.
🔭 Project Requirements
Hardware specifications
Hardware specifications
Processor: AMD Ryzen 5 or higher.
RAM: 8GB or more recommended.
Storage: At least 10GB of free space for software and models.
Camera: High-resolution camera for video streaming (optional).
GPU: NVIDIA GPU with CUDA support recommended for efficient model processing (tested on 1050.
Software specifications
Software specifications
Operating System: Windows 10
Web Framework: Flask.
Database: MySQL.
Dependencies: Flask, Flask-Migrate, OpenCV, PyTorch, YOLO, Retinaface, InceptionResnetV1, DeepSORT
📽️ Demonstration Video
Click the video above to watch a short demonstration of how the project works. 📽️
Student Profile
Project Title: Face Recognition Surveillance System
Student Name: Eshwin Der Singh A/L Talvindar Singh
Programme: BSc (Hons) Multimedia Computing (BMC)
Supervisor Name: Ivan Lau Eng Beng
Category: System and Application
Leave us your feedback 📝
*Kindly leave us your feedback and suggestion for future improvements and review in the Google form below.
Your cooperation and support are highly appreciated. Thank you!
Page updated
Report abuse