International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Wen-Chang Cheng1, Hung-Chou Hsiao2*, You-Quan Hong1, De-Yu Wang1

1 Department of Computer Science & Information Engineering, Chaoyang University of Technology, Taichung City, Taiwan

2 Department of Information Management, Chaoyang University of Technology,Taichung City, Taiwan

 

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ABSTRACT

This study proposes a face recognition method based on FaceNet for face masks. FaceNet converts faces into 128-dimensional feature vectors using a network of deep learning models. The smaller the Euclidean distance between different face images, the closer the person is to the same person; conversely, the larger the Euclidean distance, the more different the person is. Wearing a mask affects some feature vector dimensions of FaceNet conversion, resulting in reduced recognition ability. This study uses a Genetic Algorithm (GA) to select and remove the feature vectors affected by mask wearing. The experimental results show that the validation rate value of 55 features removed by the GA is increased from 0.550 to 0.650 for the original unremoved features.


Keywords: Deep learning, Genetic algorithm, Optimization algorithm, Face recognition, Euclidean distance.


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ARTICLE INFORMATION

Received: 2023-03-24
Revised: 2023-05-08
Accepted: 2023-05-22
Available Online: 2023-06-06


Cite this article:

Cheng, W.-C., Hsiao, H.-C., Hong, Y.-Q., Wang, D.-Y. Masked face recognition based on facenet and genetic algorithm. International Journal of Applied Science and Engineering, 20, 2023078. https://doi.org/10.6703/IJASE.202309_20(3).005

  Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

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