Title: Efficient masked face identification biometric systems based on ResNet and DarkNet convolutional neural networks

Authors: Freha Mezzoudj; Chahreddine Medjahed

Addresses: Department of Computer Science, University Hassiba Benbouali of Chlef, Chlef, Algeria ' Department of Computer Science, University Hassiba Benbouali of Chlef, Chlef, Algeria

Abstract: The COVID-19 pandemic has caused death and serious illness in the entire world. During humanity's fight against this disease, the wearing of face masks has become and remains a necessity in our daily life. This critical fight encourages us to generate a rich masked face database (noted FEI-SM) with different variations of poses and different emotions. We also employed several robust convolutional neural network systems based on three ResNet and two DarkNet models (ResNet18, ResNet50, ResNet101, DarkNet19, and DarkNet53) to measure the accuracy of biometric identification of masked and un-occluded faces on the challenging masked face database FEI-SM. In general, the compared results are showing good accuracies with many used biometric systems. Through experimental runs, the obtained outputs show clearly that the scheme model based on ResNet18 is the most effective model to recognise individuals with masks in different scenarios in terms of rate recognition and testing time.

Keywords: biometric; masked face identification; ResNet; DarkNet; FEI-SM database; convolutional neural networks; CNN.

DOI: 10.1504/IJCVR.2024.138306

International Journal of Computational Vision and Robotics, 2024 Vol.14 No.3, pp.284 - 303

Received: 28 Dec 2021
Accepted: 09 Sep 2022
Published online: 01 May 2024 *

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