Real-world human gender classification from oral region using convolutional neural netwrok

  • Mohamed Oulad-Kaddour
    Ecole naionale Supéerieure en Informatique m_ouled_kaddour[at]
  • Hamid Haddadou
    Ecole naionale Supéerieure en Informatique
  • Cristina Conde
    Rey Juan Carlos University
  • Daniel Palacios-Alonso
    Rey Juan Carlos University
  • Enrique Cabello
    Rey Juan Carlos University


Gender classification is an important biometric task. It has been widely studied in the literature. Face modality is the most studied aspect of human-gender classification. Moreover, the task has also been investigated in terms of different face components such as irises, ears, and the periocular region. In this paper, we aim to investigate gender classification based on the oral region. In the proposed approach, we adopt a convolutional neural network. For experimentation, we extracted the region of interest using the RetinaFace algorithm from the FFHQ faces dataset. We achieved acceptable results, surpassing those that use the mouth as a modality or facial sub-region in geometric approaches. The obtained results also proclaim the importance of the oral region as a facial part lost in the Covid-19 context when people wear facial mask. We suppose that the adaptation of existing facial data analysis solutions from the whole face is indispensable to keep-up their robustness.
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Oulad-Kaddour, M., Haddadou, H., Conde, C., Palacios-Alonso, D., & Cabello, E. (2023). Real-world human gender classification from oral region using convolutional neural netwrok . ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(3), 249–261.


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