inis 24(2): e3

Research Article

Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks

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  • @ARTICLE{10.4108/eetinis.v11i2.4318,
        author={Mohamed Oulad-Kaddour and Hamid Haddadou and Daniel Palacios-Alonso and Cristina Conde and Enrique Cabello},
        title={Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks},
        journal={EAI Endorsed Transactions on Industrial Networks and Intelligent Systems},
        volume={11},
        number={2},
        publisher={EAI},
        journal_a={INIS},
        year={2024},
        month={3},
        keywords={Gender classification, face biometrics, facial occlusions, mask-wearing, convolutional neural networks, explainable artifical intelligence},
        doi={10.4108/eetinis.v11i2.4318}
    }
    
  • Mohamed Oulad-Kaddour
    Hamid Haddadou
    Daniel Palacios-Alonso
    Cristina Conde
    Enrique Cabello
    Year: 2024
    Facial mask-wearing prediction and adaptive gender classification using convolutional neural networks
    INIS
    EAI
    DOI: 10.4108/eetinis.v11i2.4318
Mohamed Oulad-Kaddour1,*, Hamid Haddadou1, Daniel Palacios-Alonso2, Cristina Conde2, Enrique Cabello2
  • 1: École Nationale Supérieure d'Informatique
  • 2: King Juan Carlos University
*Contact email: m_ouled_kaddour@esi.dz

Abstract

The world has lived an exceptional time period caused by the Coronavirus pandemic. To limit Covid-19 propagation, governments required people to wear a facial mask outside. In facial data analysis, mask-wearing on the human face creates predominant occlusion hiding the important oral region and causing more challenges for human face recognition and categorisation. The appropriation of existing solutions by taking into consideration the masked context is indispensable for researchers. In this paper, we propose an approach for mask-wearing prediction and adaptive facial human-gender classification. The proposed approach is based on convolutional neural networks (CNNs). Both mask-wearing and gender information are crucial for various possible applications. Experimentation shows that mask-wearing is very well detectable by using CNNs and justifies its use as a prepossessing step. It also shows that retraining with masked faces is indispensable to keep up gender classification performances. In addition, experimentation proclaims that in a controlled face-pose with acceptable image quality' context, the gender attribute remains well detectable. Finally, we show empirically that the adaptive proposed approach improves global performance for gender prediction in a mixed context.