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3D palmprint identification using blocked histogram and improved sparse representation-based classifier

  • Xuefei Bai
  • Zhaozong Meng
  • Nan Gao
  • Zonghua ZhangEmail author
  • David Zhang
Original Article

Abstract

The three-dimensional (3D) palmprint-based biometrics has been an emerging approach for human recognition. However, it mainly concentrates on one-to-one verification and is not efficient for one-to-many identification. Especially, when there is a large number of samples in the database, obtaining both the accuracy and speed of recognition has been the major challenge. To handle these problems, we propose an improved sparse representation-based 3D palmprint identification scheme. The novelty of this investigation focuses on: (1) The histogram of blocked surface type and local phase of palmprint are integrated as one feature to reduce the dimensionality of 3D information and the complexity of the subsequent computations; and (2) by adding intermediate terms to the calculation of sparse coefficient, an improved sparse representation-based classifier is proposed to refine the classification results and enhance the accuracy of identification. Experimental studies demonstrate the effectiveness of the proposed scheme. Compared with other methods, the proposed solution can reduce the computational complexity and elevate the accuracy, speed, and robustness of identification. Moreover, the proposed 3D feature occupies small data volume, which makes the scheme more suitable for large-scale specimen identification applications.

Keywords

Local phase Histogram Surface type (ST) Sparse representation-based classifier (SRC) 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (51675160), the Talents Project Training Funds in Hebei Province (A201500503), and the Innovative and Entrepreneurial Talent Project Supported by Jiangsu Province (2016A377).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interests.

References

  1. 1.
    Jain AK, Nandakumar K, Ross A (2016) 50 years of biometric research: accomplishments, challenges, and opportunities. Pattern Recognit Lett 79(1):80–105CrossRefGoogle Scholar
  2. 2.
    El-Tarhouni W, Boubchir L, Elbendak M, Bouridane A (2019) Multispectral palmprint recognition using Pascal coefficients-based LBP and PHOG descriptors with random sampling. Neural Comput Appl 31(2):593–603Google Scholar
  3. 3.
    Jia W, Zhang B, Lu JT, Zhu YH, Zhao Y, Zuo WM, Ling HB (2017) Palmprint recognition based on complete direction representation. IEEE Trans Image Process 26(9):4483–4498MathSciNetCrossRefGoogle Scholar
  4. 4.
    Rida I, Herault R, Marcialis GL, Gasso G (2018) Palmprint recognition with an efficient data driven ensemble classifier. Pattern Recognit Lett.  https://doi.org/10.1016/j.patrec.2018.04.033 CrossRefGoogle Scholar
  5. 5.
    Zhang D, Lu GM (2013) 3D biometrics technologies and systems. In: 3D biometrics. Springer, New YorkCrossRefGoogle Scholar
  6. 6.
    Zhang ZH, Huang SJ, Xu YJ, Chen C, Zhao Y, Gao N, Xiao YJ (2013) 3D palmprint and hand imaging system based on full-field composite color sinusoidal fringe projection technique. Appl Opt 52(25):6138–6145CrossRefGoogle Scholar
  7. 7.
    Li W, Zhang D, Lu GM, Luo N (2012) A novel 3-D palmprint acquisition system. IEEE Trans Syst Man Cybern A Syst Humans 42(2):443–452CrossRefGoogle Scholar
  8. 8.
    PolyU 2D and 3D Palmprint Database (2018) Available: www.comp.polyu.edu.hk/~biometrics/
  9. 9.
    Zhang D, Lu GM, Li W, Zhang L, Luo N (2009) Palmprint recognition using 3-D information. IEEE Trans Syst Man Cybern C Appl Rev 39(5):505–519CrossRefGoogle Scholar
  10. 10.
    Li W, Zhang L, Zhang D, Lu GM, Yan JQ (2010) Efficient joint 2D and 3D palmprint matching with alignment refinement. In: IEEE computer society conference on computer vision and pattern recognition, pp 795–801Google Scholar
  11. 11.
    Zhang D, Kanhangad V, Luo N, Kumar A (2010) Robust palmprint verification using 2D and 3D features. Pattern Recognit 43(1):358–368CrossRefGoogle Scholar
  12. 12.
    Liu M, Li LH (2012) Cross-correlation based binary image registration for 3D palmprint recognition. In: IEEE international conference on signal processing, pp 1597–1600Google Scholar
  13. 13.
    Li W, Zhang D, Zhang L, Lu GM, Yan JQ (2011) 3-D Palmprint recognition with joint line and orientation features. IEEE Trans Syst Man Cybern C Appl Rev 41(2):274–279CrossRefGoogle Scholar
  14. 14.
    Cui JR (2014) 2D and 3D palmprint fusion and recognition using PCA plus TPTSR method. Neural Comput Appl 24(3–4):497–502CrossRefGoogle Scholar
  15. 15.
    Ni JJ, Luo J, Liu WB (2015) 3D palmprint recognition using Dempster–Shafer fusion theory. J Sensors 2015:1–7CrossRefGoogle Scholar
  16. 16.
    Yang B, Wang XH, Yao JL, Zhu WH (2013) Efficient local representations for three-dimensional palmprint recognition. J Electron Imaging 22(4):043040CrossRefGoogle Scholar
  17. 17.
    Yang B, Xiang XQ, Xu DQ, Wang XH, Yang X (2017) 3D palmprint recognition using shape index representation and fragile bits. Multimed Tools Appl 76(14):15357–15375CrossRefGoogle Scholar
  18. 18.
    Fei LK, Lu GM, Jia W, Wen J, Zhang D (2018) Complete binary representation for 3-D palmprint recognition. IEEE Trans Instrum Meas 67(12):2761–2771CrossRefGoogle Scholar
  19. 19.
    Zhang B, Li W, Qing P, Zhang D (2013) Palm-print classification by global features. IEEE Trans Syst Man Cybern Syst 43(2):370–378CrossRefGoogle Scholar
  20. 20.
    Li CY, Lu GM (2011) 3D palmprint recognition based on surface curvature and RLDA. J Image Gr 16(5):807–812Google Scholar
  21. 21.
    Zhang L, Shen Y, Li HY, Lu JW (2015) 3D Palmprint identification using block-wise features and collaborative representation. IEEE Trans Pattern Anal Mach Intell 37(8):1730–1736CrossRefGoogle Scholar
  22. 22.
    Fei LK, Zhang B, Xu Y, Jia W, Wen J, Ji JG (2019) Precision direction and compact surface type representation for 3D palmprint identification. Pattern Recognit 87:237–247CrossRefGoogle Scholar
  23. 23.
    Rajeev S, Shreyas KKM, Panetta K, Agaian S (2017) 3-D palmprint modeling for biometric verification. In: IEEE international symposium on technologies for homeland security, pp 1–6Google Scholar
  24. 24.
    Fang HL, Wang GJ (2005) Comparison and analysis of discrete curvatures estimation methods for triangular meshes. J Comput Aided Des Comput Gr 17(11):2500–2507Google Scholar
  25. 25.
    Besl PJ, Jain RC (1988) Segmentation through variable-order surface fitting. IEEE Trans Pattern Anal Mach Intell 10(2):167–192CrossRefGoogle Scholar
  26. 26.
    Cantzler H, Fisher RB (2001) Comparison of HK and SC curvature description methods. In: International conference on 3-D digital imaging and modeling, pp 285–291Google Scholar
  27. 27.
    Felsberg M, Sommer G (2001) The monogenic signal. IEEE Trans Signal Process 49(12):3136–3144MathSciNetCrossRefGoogle Scholar
  28. 28.
    Fleischmann O (2008) 2D signal analysis by generalized Hilbert transforms. Dissertation, Kiel UniversityGoogle Scholar
  29. 29.
    Stein EM, Weiss G (1971) Introduction to fourier analysis on euclidean spaces. Princeton University Press, New JerseyzbMATHGoogle Scholar
  30. 30.
    Zhang L, Ding ZX, Li HY, Shen Y (2014) 3D ear identification based on sparse representation. PLoS ONE 9(4):e95506CrossRefGoogle Scholar
  31. 31.
    Wagner A, Wright J, Ganesh A, Zhou ZH, Mobahi H, Ma Y (2012) Towards a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans Pattern Anal Mach Intell 34(2):372–386CrossRefGoogle Scholar
  32. 32.
    Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  33. 33.
    Malioutov DM, Cetin M, Willsky AS (2005) Homotopy continuation for sparse signal representation. In: IEEE international conference on acoustics, speech, and signal processing, pp v/733–v/736Google Scholar
  34. 34.
    Beck A, Teboulle M (2009) A fast iterative shrinkage thresholding algorithm for linear inverse problems. SIAM J Imaging Sci 2(1):183–202MathSciNetCrossRefGoogle Scholar
  35. 35.
    Kim SJ, Koh K, Lustig M, Boyd S, Gorinevsky D (2007) An interior-point method for large-scale L1-regularized least squares. IEEE J Sel Topics Signal Process 1(4):606–617CrossRefGoogle Scholar
  36. 36.
    Wright SJ, Nowak RD, Figueiredo MAT (2009) Sparse reconstruction by separable approximation. IEEE Trans Pattern Signal Process 57(7):2479–2493MathSciNetCrossRefGoogle Scholar
  37. 37.
    Yang JF, Zhang Y (2011) Alternating direction algorithms for L1-problems in compressive sensing. SIAM J Sci Comput 33(1):250–278MathSciNetCrossRefGoogle Scholar
  38. 38.
    Zhang L, Yang M, Feng XC (2011) Sparse representation or collaborative representation: Which helps face recognition? In: International conference on computer vision, pp 471–478Google Scholar
  39. 39.
    Chi YJ, Porikli F (2012) Connecting the dots in multi-class classification: from nearest subspace to collaborative representation. In: IEEE conference on computer vision and pattern recognition, pp 3602–3609Google Scholar
  40. 40.
    Deng WH, Hu JN, Guo J (2012) Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans Pattern Anal Mach Intell 34(9):1864–1870CrossRefGoogle Scholar
  41. 41.
    Chi YJ, Porikli F (2014) Classification and boosting with multiple collaborative representations. IEEE Trans Pattern Anal Mach Intell 36(8):1519–1531CrossRefGoogle Scholar
  42. 42.
    Zhang HJ, Wang S, Zhao MB, Xu XF, Ye YM (2018) Locality reconstruction models for book representation. IEEE Trans Knowl Data Eng 30(10):1873–1886CrossRefGoogle Scholar
  43. 43.
    Zhang HJ, Wang S, Xu XF, Chow TWS, Wu QMJ (2018) Tree2Vector: learning a vectorial representation for tree-structured data. IEEE Trans Neural Netw Learn Syst 29(11):5304–5318MathSciNetCrossRefGoogle Scholar
  44. 44.
    Zhang L, Zhou ZQ, Li HY (2012) Binary Gabor pattern: an efficient and robust descriptor for texture classification. In: IEEE international conference on image processing, pp 81–84Google Scholar
  45. 45.
    Wang YX, Ruan QQ (2009) Dual-tree complex wavelet transform based local binary pattern weighted histogram method for palmprint recognition. Comput Inform 28:299–318zbMATHGoogle Scholar
  46. 46.
    Mu MR, Ruan QQ, Shen YS (2010) Palmprint recognition based on discriminative local binary patterns statistic feature. In: International conference on signal acquisition and processing, pp 193–197Google Scholar
  47. 47.
    Jia W, Hu RX, Lei YK, Zhao Y, Gui J (2014) Histogram of oriented lines for palmprint recognition. IEEE Trans Syst Man Cybern Syst 44(3):385–395CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2020

Authors and Affiliations

  1. 1.State Key Laboratory of Reliability and Intelligence of Electrical EquipmentHebei University of TechnologyTianjinChina
  2. 2.School of Mechanical EngineeringHebei University of TechnologyTianjinChina
  3. 3.School of Science and EngineeringThe Chinese University of Hong KongShenzhenChina

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