Enhance the Performance of Directional Feature-based Palmprint Recognition by Directional Response Stability Measurement
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s11633-023-1436-6.pdf
Reference49 articles.
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2. L. K. Fei, B. Zhang, Y. Xu, C. W. Tian, I. Rida, D. Zhang. Jointly heterogeneous palmprint discriminant feature learning. IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 9, pp. 4979–4990, 2022. DOI: https://doi.org/10.1109/TNNLS.2021.3066381.
3. S. A. Maadeed, X. D. Jiang, I. Rida, A. Bouridane. Palmprint identification using sparse and dense hybrid representation. Multimedia Tools and Applications, vol. 78, no. 5, pp. 5665–5679, 2019. DOI: https://doi.org/10.1007/s11042-018-5655-8.
4. I. Rida, R. Herault, G. L. Marcialis, G. Gasso. Palmprint recognition with an efficient data driven ensemble classifier. Pattern Recognition Letters, vol. 126, pp. 21–30, 2019. DOI: https://doi.org/10.1016/j.patrec.2018.04.033.
5. W. Jia, J. Gao, W. Xia, Y. Zhao, H. Min, J. T. Lu. A performance evaluation of classic convolutional neural networks for 2D and 3D palmprint and palm vein recognition. International Journal of Automation and Computing, vol. 18, no. 1, pp. 18–44, 2021. DOI: https://doi.org/10.1007/s11633-020-1257-9.
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