Abstract
We propose a novel fast robustness palmprint recognition algorithm based on the Curvelet transform and local histogram of oriented gradient (CLHOG) for the poor curve and direction description in the traditional wavelet transform. Curvelet transform is firstly used to obtain four images with the different scales. Then, an algorithm based Local Histogram of Oriented Gradient (LHOG) is designed to extract the robust features from those different scale images. Finally, a Chi-square distance is introduced to measure the similarity in the palmprint features. The experimental results obtained through using the proposed method on both PolyU and CASIA palmprint databases are robust and superior in comparison to some high-performance algorithms.
This work was supported in part by the Natural Science Foundation of China under Grant No. 61170106 and A Project of Shandong Province Higher Educational Science and Technology Program No.J14LN39.
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Hong, D., Wu, X., Pan, Z., Su, J., Wei, W., Niu, Y. (2014). A Fast Robustness Palmprint Recognition Algorithm. In: Sun, Z., Shan, S., Sang, H., Zhou, J., Wang, Y., Yuan, W. (eds) Biometric Recognition. CCBR 2014. Lecture Notes in Computer Science, vol 8833. Springer, Cham. https://doi.org/10.1007/978-3-319-12484-1_35
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DOI: https://doi.org/10.1007/978-3-319-12484-1_35
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