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Article

Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection

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Department of Software Engineering, Kaunas University of Technology, 51368 Kaunas, Lithuania
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Department of Applied Informatics, Vytautas Magnus University, 44404 Kaunas, Lithuania
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Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland
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Department of Electrical and Information Engineering, Covenant University, Ota, Ogun State 112212, Nigeria
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Department of Computer Engineering, Atilim University, 06830 Ankara, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Rui Pedro Lopes
Electronics 2021, 10(8), 978; https://doi.org/10.3390/electronics10080978
Received: 6 March 2021 / Revised: 13 April 2021 / Accepted: 16 April 2021 / Published: 19 April 2021
(This article belongs to the Section Bioelectronics)
Face palsy has adverse effects on the appearance of a person and has negative social and functional consequences on the patient. Deep learning methods can improve face palsy detection rate, but their efficiency is limited by insufficient data, class imbalance, and high misclassification rate. To alleviate the lack of data and improve the performance of deep learning models for palsy face detection, data augmentation methods can be used. In this paper, we propose a novel Voronoi decomposition-based random region erasing (VDRRE) image augmentation method consisting of partitioning images into randomly defined Voronoi cells as an alternative to rectangular based random erasing method. The proposed method augments the image dataset with new images, which are used to train the deep neural network. We achieved an accuracy of 99.34% using two-shot learning with VDRRE augmentation on palsy faces from Youtube Face Palsy (YFP) dataset, while normal faces are taken from Caltech Face Database. Our model shows an improvement over state-of-the-art methods in the detection of facial palsy from a small dataset of face images. View Full-Text
Keywords: data augmentation; small data; Voronoi tessellation; few-shot learning; deep learning; face recognition; face palsy data augmentation; small data; Voronoi tessellation; few-shot learning; deep learning; face recognition; face palsy
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MDPI and ACS Style

Abayomi-Alli, O.O.; Damaševičius, R.; Maskeliūnas, R.; Misra, S. Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection. Electronics 2021, 10, 978. https://doi.org/10.3390/electronics10080978

AMA Style

Abayomi-Alli OO, Damaševičius R, Maskeliūnas R, Misra S. Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection. Electronics. 2021; 10(8):978. https://doi.org/10.3390/electronics10080978

Chicago/Turabian Style

Abayomi-Alli, Olusola O., Robertas Damaševičius, Rytis Maskeliūnas, and Sanjay Misra. 2021. "Few-Shot Learning with a Novel Voronoi Tessellation-Based Image Augmentation Method for Facial Palsy Detection" Electronics 10, no. 8: 978. https://doi.org/10.3390/electronics10080978

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