Abstract
In this paper, we propose a novel approach for palmprint recognition, which contains two interesting components: directional representation and compressed sensing. Gabor wavelets can be well represented for biometric image for their similar characteristics to human visual system. However, these Gabor-based algorithms are not robust for image recognition under non-uniform illumination and suffer from the heavy computational burden. To improve the recognition performance under the low quality conditions with a fast operation speed, we propose novel palmprint recognition approach using directional representations. Firstly, the directional representation for palmprint appearance is obtained by the anisotropy filter, which is robust to drastic illumination changes and preserves important discriminative information. Then, the principal component analysis (PCA) is used for feature extraction to reduce the dimensions of the palmprint images. At last, based on a sparse representation on PCA feature, the compressed sensing is used to distinguish palms from different hands. Experimental results on the PolyU palmprint database show the proposed algorithm have better performance than that of the Gabor based methods.
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Acknowledgments
This work is supported by grants by National Natural Science Foundation of China (Grant No. 51175443 and 61070163), by the Shandong Province Outstanding Research Award Fund for Young Scientists of China (Grant No. BS2011DX034) and the Shandong Natural Science Foundation (Grant No. ZR2011FQ030 and ZR2011FM023).
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Li, H., Zhang, J. & Wang, L. Robust palmprint identification based on directional representations and compressed sensing. Multimed Tools Appl 70, 2331–2345 (2014). https://doi.org/10.1007/s11042-012-1240-8
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DOI: https://doi.org/10.1007/s11042-012-1240-8