TY - JOUR
T1 - Voter Authentication Using Enhanced ResNet50 for Facial Recognition
AU - Halidou, Aminou
AU - Olle, Daniel Georges Olle
AU - Fadja, Arnaud Nguembang
AU - Kallon, Daramy Vandi Von
AU - Thibault, Tchana Ngninkeu Gil
N1 - Publisher Copyright:
© 2025 by the authors.
PY - 2025/6
Y1 - 2025/6
N2 - Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability.
AB - Electoral fraud, particularly multiple voting, undermines the integrity of democratic processes. To address this challenge, this study introduces an innovative facial recognition system that integrates an enhanced 50-layer Residual Network (ResNet50) architecture with Additive Angular Margin Loss (ArcFace) and Multi-Task Cascaded Convolutional Neural Networks (MTCNN) for face detection. Using the Mahalanobis distance, the system verifies voter identities by comparing captured facial images with previously recorded biometric features. Extensive evaluations demonstrate the methodology’s effectiveness, achieving a facial recognition accuracy of 99.85%. This significant improvement over existing baseline methods has the potential to enhance electoral transparency and prevent multiple voting. The findings contribute to developing robust biometric-based electoral systems, thereby promoting democratic trust and accountability.
KW - ArcFace loss
KW - biometric voting systems
KW - electoral fraud prevention
KW - facial recognition
KW - MTCNN
KW - ResNet50
UR - http://www.scopus.com/inward/record.url?scp=105008913493&partnerID=8YFLogxK
U2 - 10.3390/signals6020025
DO - 10.3390/signals6020025
M3 - Article
AN - SCOPUS:105008913493
SN - 2624-6120
VL - 6
JO - Signals
JF - Signals
IS - 2
M1 - 25
ER -
