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Secure and Decentralized Hybrid Multi-Face Recognition for IoT Applications.

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Summary

This study introduces a hybrid system for efficient, private multi-face recognition in Internet of Things (IoT) environments. The lightweight framework achieves over 95% accuracy on resource-constrained devices, enhancing security and scalability.

Keywords:
Internet of Thingsconvolutional neural networksdecentralizationedge AIhybrid modelmulti face-recognitionsecuritysensors

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Edge Computing

Background:

  • Smart environments and IoT increase demand for efficient, private multi-face recognition.
  • Centralized systems face latency, scalability, and security issues in IoT.
  • Decentralized solutions are needed for resource-constrained IoT devices.

Purpose of the Study:

  • To demonstrate a lightweight hybrid system for decentralized multi-face recognition in IoT.
  • To validate the feasibility and effectiveness of the proposed framework for IoT constraints.
  • To offer a privacy-preserving and scalable edge AI solution.

Main Methods:

  • Utilized a pre-trained Convolutional Neural Network (VGG16) for feature extraction.
  • Employed a Support Vector Machine (SVM) for lightweight classification.
  • Implemented on resource-constrained devices like IoT cameras and Raspberry Pi.

Main Results:

  • Achieved an average accuracy exceeding 95% on a custom dataset.
  • Successfully recognized multiple faces simultaneously in varied conditions.
  • Demonstrated real-time recognition capabilities on edge devices.

Conclusions:

  • The hybrid system is effective for decentralized multi-face recognition in IoT.
  • The architecture minimizes computational load and server dependency.
  • The solution enhances privacy and offers a scalable edge AI approach for surveillance and access control.