Advancing palmprint recognition technology through vision transformer integration

Jian He; Wei Jia; Juan Yang

doi:10.1117/12.3034744

11 July 2024 Advancing palmprint recognition technology through vision transformer integration

Jian He, Wei Jia, Juan Yang

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132102P (2024) https://doi.org/10.1117/12.3034744
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

The domain of palmprint recognition, characterized by its convenience, low privacy sensitivity, and rich feature sets, has garnered increasing research interest. Moreover, Vision Transformers (ViTs) have emerged as a promising alternative to conventional convolutional neural networks, showcasing state-of-the-art or competitive performance in various downstream image processing tasks. This paradigm shift highlights the transformative potential of ViTs in areas traditionally dominated by other neural network architectures. Despite the promising avenues opened up by ViTs, their application within the palmprint recognition field remains largely unexplored, marking a significant research gap. This paper diverges from the general analysis of ViTs to focus explicitly on their application in the domain of palmprint recognition. By undertaking a comprehensive evaluation, it establishes foundational work in assessing the effectiveness of ViT-based methodologies for palmprint recognition tasks. Concluding with a forward-looking perspective, the paper suggests future directions for research that could expanding the capabilities of ViTs in specialized areas of palmprint recognition.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jian He, Wei Jia, and Juan Yang "Advancing palmprint recognition technology through vision transformer integration", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132102P (11 July 2024); https://doi.org/10.1117/12.3034744

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KEYWORDS

Transformers

Data modeling

Machine learning

Performance modeling

Education and training

Feature extraction

Databases

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