Attention Enhanced Siamese Neural Network for Face Validation

Journal article


Yu, H. 2023. Attention Enhanced Siamese Neural Network for Face Validation. Artificial Intelligence and Applications. 2 (1), pp. 21-27. https://doi.org/10.47852/bonviewAIA32021018
AuthorsYu, H.
Abstract

Few-shot computer vision algorithms have enormous potential to produce promised results for innovative applications which only have a small volume of example data for training. Currently, the few-shot algorithm research focuses on applying transfer learning on deep neural networks that are pre-trained on big datasets. However, adapting the transformers requires highly cost computation resources. In addition, the overfitting or underfitting problems and low accuracy on large classes in the face validation domain are identified in our research. Thus, this paper proposed an alternative enhancement solution by adding contrasted attention to the negative face pairs and positive pairs to the training process. Extra attention is created through clustering-based face pair creation algorithms. The evaluation results show that the proposed approach sufficiently addressed the problems without requiring high-cost resources.

Keywords few-shot machine learning; Siamese neural network; face validation; artificial intelligence
Year2023
JournalArtificial Intelligence and Applications
Journal citation2 (1), pp. 21-27
PublisherBon View Publishing Pte Ltd
ISSN2811-0854
Digital Object Identifier (DOI)https://doi.org/10.47852/bonviewAIA32021018
Web address (URL)https://ojs.bonviewpress.com/index.php/AIA/article/view/1018
Accepted author manuscript
File Access Level
Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online14 Aug 2023
Publication process dates
Accepted24 Jul 2023
Deposited07 Mar 2024
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