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Tytuł artykułu

Palmprint Recognition Using Gabor-Based Scale Orientation

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Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Various methods are used to obtain a superior palmprint recognition system. After selecting a palmprint image filter, using Gabor orientation scale pairs is an option to support the refinement of the verification process. Many researchers use the [8×5] pair for the value of the Gabor orientation scale in the field of palmprint recognition. However, from the experiments conducted, other Gabor pairs have more impact on system improvement. The problem is to get the most suitable value pairs for palmprint applications, so in this study, a comparison of seven kinds of Gabor pairs is carried out. This Gabor pair being compared applies using original images, PCA dimension reduction, and the Euclidean method. From the research that has been done, the pair of Gabor orientation scale [8×7] or image expansion of 56 will have the most significant impact compared to other pairs. Suppose the result of this Gabor pair is [8×7] by using other improvement systems, namely the 3W filter instead of the original image, KPCA to replace the PCA, and the cosine method in the matching method. In that case, it will increase the verification value by 99.611%. The trial value obtained can be an alternative method of choice for improving palmprint recognition.
Rocznik
Strony
641--646
Opis fizyczny
Bibliogr. 25 poz., fot., tab., wyk.
Twórcy
  • Faculty of Electrical Engineering, Universitas Muhammadiyah Surakarta, Pabelan, Indonesia
Bibliografia
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  • [9] M. Aykut and M. Ekinci, Kernel Principal Component Analysis of Gabor Features for Palmprint Recognition. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009, pp. 685–694. [Online]. Available: https://doi.org/10.1007/978-3-642-01793-3_70
  • [10] Y. Xu, L. Fei, and D. Zhang, “Combining left and right palmprint images for more accurate personal identification,” IEEE Transactions on Image Processing, vol. 24, no. 2, pp. 549–559, Feb 2015.
  • [11] C. A. Perez, L. A. Cament, and L. E. Castillo, “Methodological improvement on local gabor face recognition based on feature selection and enhanced borda count,” Pattern Recognition, vol. 44, no. 4, pp. 951 – 963, 2011. [Online]. Available: //www.sciencedirect.com/science/article/pii/S0031320310005017
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  • [18] K. Sudhakar and P. Nithyanandam, “An accurate facial component detection using gabor filter,” Bulletin of Electrical Engineering and Informatics, vol. 6, no. 3, pp. 287–294, 2017.
  • [19] M. M. Hassan, H. I. Hussein, A. S. Eesa, and R. J. Mstafa, “Face recognition based on gabor feature extraction followed by fastica and lda,” Computers, Materials and Continua, vol. 68, no. 2, pp. 1637–1659, 2021.
  • [20] R. Hammouche, A. Attia, S. Akhrouf, and Z. Akhtar, “Gabor filter bank with deep autoencoder based face recognition system,” Expert Systems with Applications, p. 116743, 2022.
  • [21] X. Liang, J. Yang, G. Lu, and D. Zhang, “Compnet: Competitive neural network for palmprint recognition using learnable gabor kernels,” IEEE Signal Processing Letters, vol. 28, pp. 1739–1743, 2021.
  • [22] X. Liang, Z. Li, D. Fan, J. Li, W. Jia, and D. Zhang, “Touchless palmprint recognition based on 3d gabor template and block feature refinement,” Knowledge-Based Systems, vol. 249, p. 108855, 2022.
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  • [24] H. jun Wang, H. nian Qi, and X.-F. Wang, “A new gabor based approach for wood recognition,” Neurocomputing, vol. 116, pp. 192 – 200, 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0925231212006881
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Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-7e4c560e-4f3a-4a26-b392-487fbaac9e36
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