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Development of Surveillance Robots Based on Face Recognition Using High-Order Statistical Features and Evidence

Slim Ben Chaabane^1,2, Rafika Harrabi^1,2, Anas Bushnag¹

¹Computer Engineering Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia.

Journal of Imaging

|March 27, 2026

Summary

This summary is machine-generated.

This study introduces a cost-effective mobile surveillance robot using artificial intelligence (AI) and the Internet of Things (IoT) for intruder detection. The smart robot achieves 98.63% accuracy in face recognition, enhancing industrial security.

Keywords:

Raspberry PI classification evidence theory face recognition mass function membership degree recognition robot surveillance

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Area of Science:

Robotics and Artificial Intelligence
Computer Vision and Machine Learning
Internet of Things (IoT) Applications

Background:

Advancements in AI, CV, and IoT are transforming surveillance systems, enabling real-time processing for enhanced security.
Mobile robots are increasingly utilized in surveillance for hazardous tasks beyond human capability.
Existing systems face challenges with accuracy and robustness in dynamic environments.

Purpose of the Study:

To develop a cost-effective mobile surveillance robot prototype for industrial environments.
To integrate IoT and advanced face recognition for intelligent intruder detection.
To enhance security by differentiating between authorized personnel and intruders.

Main Methods:

A Raspberry Pi 4-based mobile robot equipped with a PIR sensor and camera for live data capture.
Utilizing IoT for real-time data transmission to a control room.
Implementing a novel face recognition algorithm combining high-order statistical features and evidence theory.
Developing a web interface for remote robot control via Wi-Fi.

Main Results:

The face recognition system demonstrated high accuracy (98.63%) in identifying individuals.
The combined approach effectively addressed variations in lighting, expressions, and occlusions.
Alert notifications with captured images were successfully sent to the control room upon detecting unfamiliar individuals.
Experimental validation with 400 images of 40 individuals confirmed the system's effectiveness.

Conclusions:

The developed mobile surveillance robot offers a reliable and accurate solution for industrial security.
The integration of AI, IoT, and robust face recognition significantly improves intruder detection capabilities.
The system's cost-effectiveness and remote control features make it suitable for diverse industrial applications.