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Dual-source discrimination power analysis for multi-instance contactless palmprint recognition

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Abstract

Due to the benefits of palmprint recognition and the advantages of biometric fusion systems, it is necessary to study multi-source palmprint fusion systems. Unfortunately, the research on multi-instance palmprint feature fusion is absent until now. In this paper, we extract the features of left and right palmprints with two-dimensional discrete cosine transform (2DDCT) to constitute a dual-source space. Normalization is utilized in dual-source space to avoid the disturbance caused by the coefficients with large absolute values. Thus complicated pre-masking is needless and arbitrary removing of discriminative coefficients is avoided. Since more discriminative coefficients can be preserved and retrieved with discrimination power analysis (DPA) from dual-source space, the accuracy performance is improved. The experiments performed on contactless palmprint database confirm that dual-source DPA, which is designed for multi-instance palmprint feature fusion recognition, outperforms single-source DPA.

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Acknowledgments

The authors would also like to thank Multimedia University in Malaysia for providing us with the palmprint database. This work was supported by National Natural Science Foundation of China (61305010, 61262019), Voyage Project of Jiangxi Province (201450), Doctoral Initiating Foundation of Nanchang Hangkong University (EA201308058), Foundation of Sichuan Development Research Center of Cultural Industries (WHCY2014A9), Key Foundation of Xihua University, and Basic Science Research Program Through the National Research Foundation of Korea (NRF) by the Ministry of Education, Science & Technology (20120192).

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Correspondence to Cheonshik Kim.

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Leng, L., Li, M., Kim, C. et al. Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimed Tools Appl 76, 333–354 (2017). https://doi.org/10.1007/s11042-015-3058-7

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  • DOI: https://doi.org/10.1007/s11042-015-3058-7

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