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KlugOculus: A Vision-Based Intelligent Architecture for Security System
Navjot Rathour1, Rajesh Singh1, Anita Gehlot1
1Division of Research & Innovation, Uttaranchal University, Dehradun, Uttarakhand 248007, India.
Computational Intelligence and Neuroscience
|May 27, 2022
Summary
This study introduces a vision-based system for intruder detection using facial recognition. The proposed architecture achieves 99.9% accuracy, enhancing security systems with reliable identification capabilities.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Security Systems
Background:
- Traditional security systems face limitations in computational power for real-time facial recognition.
- Adaptive video analytics and machine learning are emerging for enhanced activity detection.
Purpose of the Study:
- To propose a vision-based intelligent architecture for intruder detection using facial recognition.
- To develop a system with customized hardware for improved security applications.
Main Methods:
- Facial recognition implemented using machine learning (ML) inspired Support Vector Machine (SVM) and Histogram of Oriented Gradients (HOG).
- System trained with 120 images across 20 subjects.
- Real-time implementation on Raspberry Pi 3 hardware.
Main Results:
- Achieved 99.9% accuracy in intruder identification for 20 subjects.
- Demonstrated high accuracy (99.9%) even with tilted subject images.
- Successful real-time facial recognition on embedded hardware.
Conclusions:
- The proposed vision-based system offers a highly accurate and robust solution for intruder detection.
- The system's performance, even with image variations, makes it suitable for adoption by security personnel.
- This technology can significantly boost existing security infrastructure for identification purposes.
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