Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition

Authors

  • Faisal Dharma Adhinata Institut Teknologi Telkom Purwokerto http://orcid.org/0000-0002-2624-173X
  • Nia Annisa Ferani Tanjung Institut Teknologi Telkom Purwokerto
  • Widi Widayat Institut Teknologi Telkom Purwokerto
  • Gracia Rizka Pasfica Institut Teknologi Telkom Purwokerto
  • Fadlan Raka Satura Institut Teknologi Telkom Purwokerto

DOI:

https://doi.org/10.26555/jiteki.v7i2.20758

Keywords:

Coronavirus, Face Recognition, MobileNetV2, Transfer Learning, VGG16

Abstract

Indonesia is one of the countries affected by the coronavirus pandemic, which has taken too many lives. The coronavirus pandemic forces us to continue to wear masks daily, especially when working to break the chain of the spread of the coronavirus. Before the pandemic, face recognition for attendance used the entire face as input data, so the results were accurate. However, during this pandemic, all employees use masks, including attendance, which can reduce the level of accuracy when using masks. In this research, we use a deep learning technique to recognize masked faces. We propose using transfer learning pre-trained models to perform feature extraction and classification of masked face image data. The use of transfer learning techniques is due to the small amount of data used. We analyzed two transfer learning models, namely VGG16 and MobileNetV2. The parameters of batch size and number of epochs were used to evaluate each model. The best model is obtained with a batch size value of 32 and the number of epochs 50 in each model. The results showed that using the MobileNetV2 model was more accurate than VGG16, with an accuracy value of 95.42%. The results of this study can provide an overview of the use of transfer learning techniques for masked face recognition.

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Published

2021-07-20

How to Cite

[1]
F. D. Adhinata, N. A. F. Tanjung, W. Widayat, G. R. Pasfica, and F. R. Satura, “Comparative Study of VGG16 and MobileNetV2 for Masked Face Recognition”, J. Ilm. Tek. Elektro Komput. Dan Inform, vol. 7, no. 2, pp. 230–237, Jul. 2021.

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Vol. 7 No. 2 (2021): August

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