Masked Faces: Overcoming Recognition Challenges with Transfer Learning in CNNs

  • Italo T. Noé Universidade Federal de Ouro Preto (UFOP)
  • Lucas H. L. Costa Universidade Federal de Ouro Preto (UFOP)
  • Talles H. Medeiros Universidade Federal de Ouro Preto (UFOP)


Amidst the coronavirus pandemic, the use of masks has become one of the main ways to prevent and control the spread of this virus. However, masks impacted the performance of several face recognition models, by reducing visible features in images. The objective of this work is to present a model of convolutional neural networks with transfer learning capable of classifying thirty individuals regardless of the use of masks. The model was trained in a dataset with real images of masks and another with the insertion of computationally simulated masks and the accuracy results obtained were greater than 90%. Due to the number of classes and limitations of the dataset used, the result is consistent with the low number of related works to facial recognition with masks and highlights the complexity of the problem. It is believed that the use of a dataset with superior quality and quantity of images would make the use of the model more viable for the real world, but the tests presented are promising.

Palavras-chave: Convolutional Neural Networks, Facial Recognition, Transfer learning


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NOÉ, Italo T.; COSTA, Lucas H. L.; MEDEIROS, Talles H.. Masked Faces: Overcoming Recognition Challenges with Transfer Learning in CNNs. In: SYMPOSIUM ON KNOWLEDGE DISCOVERY, MINING AND LEARNING (KDMILE), 11. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 25-32. ISSN 2763-8944. DOI: