Secured Voting System using Proposed MTCNN, Retinaface, Dlib CNN Face Detector

DOI

10.2991/978-94-6463-858-5_90How to use a DOI?

Keywords

Facial Recognition; Smart Voting System; OpenCV; MTCNN(Multi-task Cascaded Convolutional Networks); RetinaFace; Dlib CNN; Facial Landmarks

Abstract

The traditional voting process face challenges like long queues, manual identity verification, and human error, reducing accessibility and voter turnout. To address these issues, a Facial Recognition-based Smart Voting System is proposed, using advanced machine learning. The system employs Python’s OpenCV and the MTCNN model for face detection and alignment, enhanced by pre-trained models like RetinaFace and Dlib CNN for robustness. It captures live facial images, extracts key landmarks, and compares facial embedding’s with stored data for voter authentication. This approach improves accuracy, security, and efficiency, fostering greater accessibility and trust in elections.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Bandaru Shanmukha Priya
AU  - K. Navya
AU  - P. Harichandana
AU  - V. Shivani
AU  - N. Sudeshna
PY  - 2025
DA  - 2025/11/04
TI  - Secured Voting System using Proposed MTCNN, Retinaface, Dlib CNN Face Detector
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1086
EP  - 1096
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_90
DO  - 10.2991/978-94-6463-858-5_90
ID  - Priya2025
ER  -