Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Jun 2;20(6):e0323373.
doi: 10.1371/journal.pone.0323373. eCollection 2025.

A high-efficiency palmprint recognition model integrating ROI and Gabor filtering

Nan Zhang  1 , Maolong Xi  1
Affiliations

A high-efficiency palmprint recognition model integrating ROI and Gabor filtering

Nan Zhang et al. PLoS One. .

Abstract

Palmprint recognition, as a biometric recognition technology, has unique individual recognition and high accuracy, and is broadly utilized in fields such as identity verification and security monitoring. Therefore, a palm print recognition model that integrates regions of interest and Gabor filters has been proposed to solve the problem of difficulty in feature extraction caused by factors such as noise, lighting changes, and acquisition angles that often affect palm print images during the acquisition process. This model extracts standardized feature regions of palmprint images through the region of interest method, enhances texture features through multi-scale Gabor filters, and finally uses support vector machines for classification. The experiment findings denote that the region of interest model performs better than other methods in terms of signal-to-noise ratio and root mean square error, with a signal-to-noise ratio of 0.89 on the GPDS dataset and 0.97 on the CASIA dataset. The proposed model performs the best in recognition accuracy and error convergence speed, with a final accuracy of 95%. The proposed model has the shortest running time, less than 0.4 seconds in all groups, especially less than 0.3 seconds in Group 4, demonstrating high recognition efficiency. The research conclusion shows that the palmprint recognition method combining regions of interest and Gabor filters has high efficiency and performance, and can effectively improve recognition accuracy.

PubMed Disclaimer

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. SPEM-ROI-based palmprint extraction process.
Fig 2
Fig 2. Schematic diagram of automatic WB algorithm.
Fig 3
Fig 3. ROI image extraction process.
Fig 4
Fig 4. Structure of palmprint recognition model based on Gabor filter.
Fig 5
Fig 5. SVM schematic diagram.
Fig 6
Fig 6. Gabor-SVM model structure.
Fig 7
Fig 7. Comparison of SNR and SIL among various models.
Fig 8
Fig 8. Performance analysis of various models at different iterations.
Fig 9
Fig 9. Analysis of processing time for each model.
Fig 10
Fig 10. Comparison of ACC and RMSE of various recognition models.
Fig 11
Fig 11. Comparison of processing time for various models.
Fig 12
Fig 12. Comparative analysis of palmprints of various models.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Bangaru SS, Wang C, Aghazadeh F. Automated and continuous fatigue monitoring in construction workers using forearm EMG and IMU wearable sensors and recurrent neural network. Sensors (Basel). 2022;22(24):9729. doi: 10.3390/s22249729 - DOI - PMC - PubMed
    1. Miao X, Xue C, Li X, Yang L. A real-time fatigue sensing and enhanced feedback system. Information. 2022;13(5):230. doi: 10.3390/info13050230 - DOI
    1. Chen J, Yan M, Zhu F, Xu J, Li H, Sun X. Fatigue driving detection method based on combination of BP neural network and time cumulative effect. Sensors (Basel). 2022;22(13):4717. doi: 10.3390/s22134717 - DOI - PMC - PubMed
    1. Varandas R, Lima R, Bermúdez I Badia S, Silva H, Gamboa H. Automatic cognitive fatigue detection using wearable fnirs and machine learning. Sensors (Basel). 2022;22(11):4010. doi: 10.3390/s22114010 - DOI - PMC - PubMed
    1. Min J, Cai M, Gou C, Xiong C, Yao X. Fusion of forehead EEG with machine vision for real-time fatigue detection in an automatic processing pipeline. Neural Comput Appl. 2022;35(12):8859–72. doi: 10.1007/s00521-022-07466-0 - DOI

LinkOut - more resources