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Multimedia Tools and Applications

, Volume 78, Issue 5, pp 5665–5679 | Cite as

Palmprint identification using sparse and dense hybrid representation

  • Somaya Al MaadeedEmail author
  • Xudong Jiang
  • Imad Rida
  • Ahmed Bouridane
Article

Abstract

Among various palmprint identification methods proposed in the literature, Sparse Representation for Classification (SRC) is very attractive, offering high accuracy. Although SRC has good discriminative ability, its performance strongly depends on the quality of the training data. In fact, palmprint images do not only contain identity information but they also have other information such as illumination and distortions due the acquisition conditions. In this case, SRC may not be able to classify the identity of palmprint well in the original space since samples from the same class show large variations. To overcome this problem, we propose in this work to exploit sparse-and-dense hybrid representation (SDR) for palmprint identification. Indeed, this type of representations that are based on the dictionary learning from the training data has shown its great advantage to overcome the limitations of SRC. Extensive experiments are conducted on two publicly available palmprint datasets: multispectral and PolyU. The obtained results clearly show the ability of the proposed method to outperform both the state-of-the-art holistic approaches and the coding palmprint identification methods.

Keywords

Biometric Palmprint Sparse representation for classification 

Notes

Acknowledgments

This publication was made possible using a grant from the Qatar National Research Fund through National Priority Research Program (NPRP) No. 6-249-1-053. The contents of this publication are solely the responsibility of the authors and do not necessarily represent the official views of the Qatar National Research Fund or Qatar University.

References

  1. 1.
    Badrinath G, Gupta P (2008) Palmprint verification using sift features. In: First workshops on image processing theory, tools and applications, 2008. IPTA 2008. IEEE, pp 1–8Google Scholar
  2. 2.
    Bertsekas DP (2014) Constrained optimization and Lagrange multiplier methods. Academic Press, New YorkzbMATHGoogle Scholar
  3. 3.
    Charfi N, Trichili H, Alimi AM, Solaiman B (2017) Bimodal biometric system for hand shape and palmprint recognition based on sift sparse representation. Multimedia Tools and Applications 76(20):20,457–20,482CrossRefGoogle Scholar
  4. 4.
    Connie T, Jin ATB, Ong MGK, Ling DNC (2005) An automated palmprint recognition system. Image Vis Comput 23(5):501–515CrossRefGoogle Scholar
  5. 5.
    Cui J, Wen J, Fan Z (2015) Appearance-based bidirectional representation for palmprint recognition. Multimedia Tools and Applications 74(24):10,989–11,001CrossRefGoogle Scholar
  6. 6.
    De Marsico M, Nappi M, Riccio D, Wechsler H (2013) Robust face recognition for uncontrolled pose and illumination changes. IEEE Trans Syst Man Cybern 43 (1):149–163CrossRefGoogle Scholar
  7. 7.
    Fei L, Teng S, Wu J, Rida I (2017) Enhanced minutiae extraction for high-resolution palmprint recognition. International Journal of Image and Graphics 17 (04):1750,020CrossRefGoogle Scholar
  8. 8.
    Fei L, Xu Y, Tang W, Zhang D (2016) Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recogn 49:89–101CrossRefGoogle Scholar
  9. 9.
    Fei L, Xu Y, Zhang B, Fang X, Wen J (2016) Low-rank representation integrated with principal line distance for contactless palmprint recognition. Neurocomputing 218:264–275CrossRefGoogle Scholar
  10. 10.
    Fei L, Xu Y, Zhang D (2016) Half-orientation extraction of palmprint features. Pattern Recogn Lett 69:35–41CrossRefGoogle Scholar
  11. 11.
    Guo X, Zhou W, Zhang Y (2017) Collaborative representation with hm-lbp features for palmprint recognition. Mach Vis Appl 28(3-4):283–291CrossRefGoogle Scholar
  12. 12.
    Guo Z, Zhang D, Zhang L, Zuo W (2009) Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn Lett 30(13):1219–1227CrossRefGoogle Scholar
  13. 13.
    Hammami M, Jemaa SB, Ben-Abdallah H (2014) Selection of discriminative sub-regions for palmprint recognition. Multimedia Tools and Applications 68(3):1023–1050CrossRefGoogle Scholar
  14. 14.
    Han CC, Cheng HL, Lin CL, Fan KC (2003) Personal authentication using palm-print features. Pattern Recogn 36(2):371–381CrossRefGoogle Scholar
  15. 15.
    Hennings-Yeomans PH, Kumar BV, Savvides M (2007) Palmprint classification using multiple advanced correlation filters and palm-specific segmentation. IEEE Trans Inf Forensics Secur 2(3):613–622CrossRefGoogle Scholar
  16. 16.
    Hong D, Liu W, Su J, Pan Z, Wang G (2015) A novel hierarchical approach for multispectral palmprint recognition. Neurocomputing 151:511–521CrossRefGoogle Scholar
  17. 17.
    Hong D, Liu W, Wu X, Pan Z, Su J (2016) Robust palmprint recognition based on the fast variation vese–osher model. Neurocomputing 174:999–1012CrossRefGoogle Scholar
  18. 18.
    Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2dlpp) with its application to palmprint recognition. Pattern Recogn 40(1):339–342zbMATHCrossRefGoogle Scholar
  19. 19.
    Huang DS, Jia W, Zhang D (2008) Palmprint verification based on principal lines. Pattern Recogn 41(4):1316–1328CrossRefGoogle Scholar
  20. 20.
    Jia W, Huang DS, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recogn 41(5):1504–1513zbMATHCrossRefGoogle Scholar
  21. 21.
    Jia W, Zhang B, Lu J, Zhu Y, Zhao Y, Zuo W, Ling H (2017) Palmprint recognition based on complete direction representation. IEEE Trans Image Process 26(9):4483–4498MathSciNetzbMATHCrossRefGoogle Scholar
  22. 22.
    Jiang X, Lai J (2015) Sparse and dense hybrid representation via dictionary decomposition for face recognition. IEEE Trans Pattern Anal Mach Intell 37 (5):1067–1079CrossRefGoogle Scholar
  23. 23.
    Jing XY, Zhang D (2004) A face and palmprint recognition approach based on discriminant dct feature extraction. IEEE Trans Syst Man Cybern B (Cybern) 34 (6):2405–2415CrossRefGoogle Scholar
  24. 24.
    Kong A, Zhang D, Kamel M (2006) Palmprint identification using feature-level fusion. Pattern Recogn 39(3):478–487zbMATHCrossRefGoogle Scholar
  25. 25.
    Kong AK, Zhang D (2004) Competitive coding scheme for palmprint verification. In: Proceedings of the 17th international conference on pattern recognition, 2004. ICPR 2004, vol 1. IEEE, pp 520–523Google Scholar
  26. 26.
    Laadjel M, Al-Maadeed S, Bouridane A (2015) Combining fisher locality preserving projections and passband dct for efficient palmprint recognition. Neurocomputing 152:179–189CrossRefGoogle Scholar
  27. 27.
    Lai J, Jiang X (2016) Classwise sparse and collaborative patch representation for face recognition. IEEE Trans Image Process 25(7):3261–3272MathSciNetzbMATHCrossRefGoogle Scholar
  28. 28.
    Leng L, Li M, Kim C, Bi X (2017) Dual-source discrimination power analysis for multi-instance contactless palmprint recognition. Multimedia Tools and Applications 76(1):333–354CrossRefGoogle Scholar
  29. 29.
    Li G, Kim J (2017) Palmprint recognition with local micro-structure tetra pattern. Pattern Recogn 61:29–46CrossRefGoogle Scholar
  30. 30.
    Li H, Wang L (2012) Palmprint recognition using dual-tree complex wavelet transform and compressed sensing. In: International conference on measurement, information and control (MIC), 2012, vol 2. IEEE, pp 563–567Google Scholar
  31. 31.
    Lu G, Zhang D, Wang K (2003) Palmprint recognition using eigenpalms features. Pattern Recogn Lett 24(9):1463–1467zbMATHCrossRefGoogle Scholar
  32. 32.
    Luo YT, Zhao LY, Zhang B, Jia W, Xue F, Lu JT, Zhu YH, Xu BQ (2016) Local line directional pattern for palmprint recognition. Pattern Recogn 50:26–44CrossRefGoogle Scholar
  33. 33.
    Meraoumia A, Chitroub S, Bouridane A (2015) Do multispectral palmprint images be reliable for person identification? Multimedia Tools and Applications 74 (3):955–978CrossRefGoogle Scholar
  34. 34.
    Mokni R, Drira H, Kherallah M (2016) Combining shape analysis and texture pattern for palmprint identification. Multimedia Tools and Applications 76 (22):23981–24008CrossRefGoogle Scholar
  35. 35.
    Mokni R, Kherallah M (2016) Palmprint identification using glcm texture features extraction and svm classifier. Journal of Information Assurance & Security 11(2):77–86Google Scholar
  36. 36.
    Mu M, Ruan Q, Guo S (2011) Shift and gray scale invariant features for palmprint identification using complex directional wavelet and local binary pattern. Neurocomputing 74(17):3351–3360CrossRefGoogle Scholar
  37. 37.
    Naseem I, Togneri R, Bennamoun M (2010) Linear regression for face recognition. IEEE Trans Pattern Anal Mach Intell 32(11):2106–2112CrossRefGoogle Scholar
  38. 38.
    Raghavendra R, Busch C (2014) Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition. Pattern Recogn 47(6):2205–2221CrossRefGoogle Scholar
  39. 39.
    Raghavendra R, Busch C (2015) Texture based features for robust palmprint recognition: a comparative study. EURASIP J Inf Secur 2015(1):5CrossRefGoogle Scholar
  40. 40.
    Rida I, Almaadeed S, Bouridane A (2016) Gait recognition based on modified phase-only correlation. Signal, Image and Video Processing 10(3):463–470CrossRefGoogle Scholar
  41. 41.
    Rida I, Al-Maadeed S, Mahmood A, Bouridane A, Bakshi S. Palmprint Identification Using an Ensemble of Sparse Representations,  https://doi.org/10.1109/ACCESS.2017.2787666, IEEE AccessCrossRefGoogle Scholar
  42. 42.
    Rida I, Jiang X, Marcialis GL (2016) Human body part selection by group lasso of motion for model-free gait recognition. IEEE Signal Process Lett 23(1):154–158CrossRefGoogle Scholar
  43. 43.
    Rigamonti R, Brown MA, Lepetit V (2011) Are sparse representations really relevant for image classification?. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011. IEEE, pp 1545–1552Google Scholar
  44. 44.
    Sang H, Yuan W, Zhang Z (2009) Research of palmprint recognition based on 2dpca. In: International symposium on neural networks. Springer, pp 831–838Google Scholar
  45. 45.
    Shi Q, Eriksson A, Van Den Hengel A, Shen C (2011) Is face recognition really a compressive sensing problem?. In: IEEE conference on computer vision and pattern recognition (CVPR), 2011. IEEE, pp 553–560Google Scholar
  46. 46.
    Srinivas BG, Gupta P (2009) Palmprint based verification system using surf features. Contemporary Computing 250–262Google Scholar
  47. 47.
    Sun Z, Tan T, Wang Y, Li SZ (2005) Ordinal palmprint represention for personal identification [represention read representation]. In: Computer vision and pattern recognition, 2005. CVPR 2005, vol 1. IEEE, pp 279–284Google Scholar
  48. 48.
    Sun Z, Wang L, Tan T (2014) Ordinal feature selection for iris and palmprint recognition. IEEE Trans Image Process 23(9):3922–3934MathSciNetzbMATHCrossRefGoogle Scholar
  49. 49.
    Tabejamaat M, Mousavi A (2017) Concavity-orientation coding for palmprint recognition. Multimedia Tools and Applications 76(7):9387–9403CrossRefGoogle Scholar
  50. 50.
    Tabejamaat M, Mousavi A (2017) Manifold sparsity preserving projection for face and palmprint recognition. Multimedia Tools and Applications 1–26Google Scholar
  51. 51.
    Tamrakar D, Khanna P (2015) Occlusion invariant palmprint recognition with ulbp histograms. Procedia Computer Science 54:491–500CrossRefGoogle Scholar
  52. 52.
    Tamrakar D, Khanna P (2016) Kernel discriminant analysis of block-wise gaussian derivative phase pattern histogram for palmprint recognition. J Vis Commun Image Represent 40:432– 448CrossRefGoogle Scholar
  53. 53.
    Tamrakar D, Khanna P (2016) Noise and rotation invariant rdf descriptor for palmprint identification. Multimedia Tools and Applications 75(10):5777–5794CrossRefGoogle Scholar
  54. 54.
    Wang M, Ruan Q (2006) Palmprint recognition based on two-dimensional methods. In: 8th international conference on signal processing, 2006, vol 4. IEEEGoogle Scholar
  55. 55.
    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
  56. 56.
    Wu X, Zhang D, Wang K (2003) Fisherpalms based palmprint recognition. Pattern Recogn Lett 24(15):2829–2838CrossRefGoogle Scholar
  57. 57.
    Xu Y, Fan Z, Qiu M, Zhang D, Yang JY (2013) A sparse representation method of bimodal biometrics and palmprint recognition experiments. Neurocomputing 103:164–171CrossRefGoogle Scholar
  58. 58.
    Zhang D, Guo Z, Lu G, Zhang L, Zuo W (2010) An online system of multispectral palmprint verification. IEEE Trans Instrum Meas 59(2):480–490CrossRefGoogle Scholar
  59. 59.
    Zhang D, Kong WK, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050CrossRefGoogle Scholar
  60. 60.
    Zhang L, Li H, Niu J (2012) Fragile bits in palmprint recognition. IEEE Signal Process Lett 19(10):663–666CrossRefGoogle Scholar
  61. 61.
    Zhang L, Shen Y, Li H, Lu J (2015) 3d palmprint identification using block-wise features and collaborative representation. IEEE Trans Pattern Anal Mach Intell 37(8):1730–1736CrossRefGoogle Scholar
  62. 62.
    Zheng Q, Kumar A, Pan G (2016) Suspecting less and doing better: New insights on palmprint identification for faster and more accurate matching. IEEE Trans Inf Forensics Secur 11(3):633– 641CrossRefGoogle Scholar
  63. 63.
    Zuo W, Yue F, Wang K, Zhang D (2008) Multiscale competitive code for efficient palmprint recognition. In: 19th international conference on pattern recognition, 2008. ICPR 2008. IEEE, pp 1–4Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Somaya Al Maadeed
    • 1
    Email author
  • Xudong Jiang
    • 2
  • Imad Rida
    • 1
  • Ahmed Bouridane
    • 3
  1. 1.Department of Computer Science and EngineeringQatar UniversityDohaQatar
  2. 2.School of Electrical and Electronics EngineeringNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of Computer Science and Digital TechnologiesNorthumbria UniversityNewcastle upon TyneUK

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