Secure and decentralized hybrid multi-face recognition for IoT applications
Loading...
Author (Corporation)
Publication date
2025
Type of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Sensors
Special issue
DOI of the original publication
Link
Related research data
Series
Series number
Volume
25
Issue / Number
18
Pages / Duration
Patent number
Publisher / Publishing institution
MDPI
Place of publication / Event location
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
The proliferation of smart environments and Internet of Things (IoT) applications has intensified the demand for efficient, privacy-preserving multi-face recognition systems. Conventional centralized systems suffer from latency, scalability, and security vulnerabilities. This paper presents a practical hybrid multi-face recognition framework designed for decentralized IoT deployments. Our approach leverages a pre-trained Convolutional Neural Network (VGG16) for robust feature extraction and a Support Vector Machine (SVM) for lightweight classification, enabling real-time recognition on resource-constrained devices such as IoT cameras and Raspberry Pi boards. The purpose of this work is to demonstrate the feasibility and effectiveness of a lightweight hybrid system for decentralized multi-face recognition, specifically tailored to the constraints and requirements of IoT applications. The system is validated on a custom dataset of 20 subjects collected under varied lighting conditions and facial expressions, achieving an average accuracy exceeding 95% while simultaneously recognizing multiple faces. Experimental results demonstrate the system’s potential for real-world applications in surveillance, access control, and smart home environments. The proposed architecture minimizes computational load, reduces dependency on centralized servers, and enhances privacy, offering a promising step toward scalable edge AI solutions.
Keywords
Internet of Things, convolutional neural networks, decentralization, edge AI, hybrid model, multi face-recognition, security, sensors
Event
Exhibition start date
Exhibition end date
Conference start date
Conference end date
Date of the last check
ISBN
ISSN
1424-8220
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
peer-reviewed
Open access category
Gold
License
Citation
Abdullahu, E., Wache, H., & Piangerelli, M. (2025). Secure and decentralized hybrid multi-face recognition for IoT applications. Sensors, 25(18). https://doi.org/10.3390/s25185880