| Issue |
ITM Web Conf.
Volume 85, 2026
Intelligent Systems for a Sustainable Future (ISSF 2026)
|
|
|---|---|---|
| Article Number | 01004 | |
| Number of page(s) | 6 | |
| Section | AI for Healthcare, Agriculture, Smart Society & Computer Vision | |
| DOI | https://doi.org/10.1051/itmconf/20268501004 | |
| Published online | 09 April 2026 | |
Resource-efficient face verification for edge devices using mmwave-triggered vision
1 Dept of ECE, SRM Valliammai Engineering College, Tamil Nadu, India
2 Dept of ECE, SRM Valliammai Engineering College, Tamil Nadu, India
3 Dept of ECE, SRM Valliammai Engineering College, Tamil Nadu, India
4 Dept of ECE, SRM Valliammai Engineering College, Tamil Nadu, India
5 Dept of ECE, SRM Valliammai Engineering College, Tamil Nadu, India
This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
This email address is being protected from spambots. You need JavaScript enabled to view it.
Abstract
This work presents the implementation of a face verification system designed for edge hardware, where mmWave radar is used to trigger image capture and a compressed deep learning model performs local recognition. The system integrates a 24 GHz mmWave sensor for intelligent human presence detection, an ESP32-CAM module for image capture and preprocessing, and an ESP32-S3 microcontroller for real-time face embedding extraction and matching. A knowledge distillation framework compresses a MobileFaceNet 1.0× model into lightweight student variants enabling INT8 quantized inference fully on-device. Experimental results demonstrate sub-second recognition delay with minimal accuracy degradation, making the system suitable for smart home and IoT-based access control applications.
© The Authors, published by EDP Sciences, 2026
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.