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Palmprint identification combining hierarchical multi-scale complete LBP and weighted SRC

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Abstract

Palmprint is one of the most reliable biometrics and has been widely used for human identification due to its high recognition accuracy and convenience for practical application. But the existing palmprint-based human identification system often suffers from image misalignment, pixel corruption and much computational time on the large database. An effective palmprint recognition method is proposed by combining hierarchical multi-scale complete local binary pattern (HMS-CLBP) and weighted sparse representation-based classification (WSRC). The hierarchical multi-scale local invariant texture features are extracted firstly from each palmprint by multi-scale local binary pattern (MS-LBP) and multi-scale complete local binary pattern (MS-CLBP) and are concatenated into one hierarchical multi-scale fusion feature vector. Then, WSRC is constructed by the Gaussian kernel distance, and use the Gaussian kernel distances between the fusion feature vectors of the training and testing samples. Finally, the sparse decomposition of testing samples is implemented by solving the optimization problem based on l1 norm, and the palmprints are recognized by the minimum residuals. The proposed method inherits the advantages of CLBP and WSRC and has good rotation, illumination and occlusion invariance. The results on the PolyU and CASIA palmprint databases illustrate the good performance and rationale interpretation of the proposed method.

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References

  1. Abukmeil MAM, Hatem E, Alhanjouri M (2015) Palmprint recognition via bandlet, ridgelet, wavelet and neural network. J Comput Sci Appl 3(2):23–28

  2. Chan CH, Kittler J (2010) Sparse representation of (multiscale) histograms for face recognition robust to registration and illumination problems. In: IEEE 17th international conference on image processing (ICIP 2010), pp 3009–3012

  3. Chen C, Zhang B, Su H et al (2015) Land-use scene classification using multi-scale completed local binary patterns. Signal Image Video Process 10(4):745–752

  4. Dai X, Wang B, Wang P (2010) Palmprint recognition combining LBP and cellular automata. Lect Notes Comput Sci 6215:460–466

  5. Dexing Z, Xuefeng D, Kuncai Z (2018) Decade progress of palmprint recognition: a brief survey. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.03.081

  6. El-Tarhouni W, Boubchir L, Elbendak M et al (2017) Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling. Neural Comput Appl 4:1–11. https://doi.org/10.1007/s00521-017-3092-7

  7. Fan Z, Ming N, Qi Z et al (2015) Weighted sparse representation for face recognition. Neurocomputing 151(1):304–309

  8. Guo X, Zhou W, Zhang Y (2017) Collaborative representation with HM-LBP features for palmprint recognition. Mach Vis Appl 28(3–4):283–291

  9. Huang L, Chen C, Li W et al (2016) Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors. Remote Sens 8(6):483. https://doi.org/10.3390/rs8060483

  10. Jia Q, Gao X, Guo H et al (2015) Multi-layer sparse representation for weighted LBP-blocks based facial expression recognition. Sensors (Basel) 15(3):6719–6739

  11. Kong A, Zhang D, Kamel M (2009) A survey of palmprint recognition. Pattern Recognit 42(7):1408–1418

  12. Kylberg G, Sintorn IM (2013) Evaluation of noise robustness for local binary pattern descriptors in texture classification. EURASIP J Image Video Process 1:17. https://doi.org/10.1186/1687-5281-2013-17

  13. Lim ST, Ahmed MK, Lim SL (2017) Automatic classification of diabetic macular EDEMA using a modified completed local binary pattern (CLBP). In: IEEE international conference on signal and image processing applications. https://doi.org/10.1109/icsipa.2017.8120570

  14. Lu CY, Min H, Gui J et al (2013) Face recognition via weighted sparse representation. J Vis Commun Image Represent 24(2):111–116

  15. Lu Z, Xu B, Liu N et al (2017) Face recognition via weighted sparse representation using metric learning. In: IEEE International conference on multimedia and expo (ICME), Hong Kong, pp 391–396

  16. Luo YT, Zhao LY, Zhang B (2016) Local line directional pattern for palmprint recognition. Pattern Recognit 50:26–44

  17. Mansoor AB, Masood H, Mumtaz M et al (2011) A feature level multimodal approach for palmprint identification using directional subband energies. J Netw Comput Appl 34(1):159–171

  18. Mu M, Ruan Q, Shen Y (2010) Palmprint recognition based on discriminative local binary patterns statistic feature. In: International conference on signal acquisition and processing. https://doi.org/10.1109/icsap.20

  19. Ouyang Y, Sang N (2013) A facial expression recognition method by fusing multiple sparse representation based classifiers. In: Proceedings of the 10th international symposium on neural networks, pp 479–488

  20. Raghavendra R, Busch C (2015) Texture based features for robust palmprint recognition: a comparative study. EURASIP J Inf Secur 2015(1):5. https://doi.org/10.1186/s13635-015-0022-z

  21. Sehgal P (2015) Palm recognition using LBP and SVM. Int J Inf Technol Syst 4(1):35–41

  22. Song KC, Yan YH, Chen WH et al (2013) Research and perspective on local binary pattern. Acta Autom Sin 39(6):730–744

  23. Tamrakar D, Khanna P (2015) Occlusion invariant palmprint recognition with ULBP histograms. Procedia Comput Sci 54:491–500

  24. Wang W, Jin W, Xie Y et al (2014) Palmprint recognition using uniform local binary patterns and sparse representation. Opto Electron Eng 41(12):60–65

  25. Xu Y, Fan Z, Qiu M et al (2013) A sparse representation method of bimodal biometrics and palmprint recognition experiments. Neurocomputing 103(2):164–171

  26. Yassir A, Larbi B, Boubaker D (2019) Multispectral palmprint recognition: a survey and comparative study. J Circuits Syst Comput 28(07):1950107

  27. Yin HF, Wu XJ (2013) A new weighted sparse representation based on MSLBP and its application to face recognition. Partial Superv Learn LNAI 8183:104–115

  28. Zhang D, Zuo W, Yue F (2012) A comparative study of palmprint recognition algorithms. ACM Comput Surv 44(1):1–37

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Acknowledgements

This work is partially supported by the China National Natural Science Foundation under Grant Nos. 61473237. The authors would like to thank all the editors and anonymous reviewers for their constructive advices. The authors would like to thank the Hong Kong Polytechnic University (PolyU) and Institute of Automation, Chinese Academy of Sciences (CASIA) for sharing their palmprint databases with us.

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Correspondence to Harry Wang.

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The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.

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Zhang, S., Wang, H. & Huang, W. Palmprint identification combining hierarchical multi-scale complete LBP and weighted SRC. Soft Comput 24, 4041–4053 (2020). https://doi.org/10.1007/s00500-019-04172-3

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Keywords

  • Palmprint recognition
  • Local binary pattern (LBP)
  • Complete LBP (CLBP)
  • Hierarchical multi-scale CLBP (HMS-CLBP)
  • Sparse representation-based classification (SRC)
  • Weighted SRC (WSRC)