Skip to main content
Log in

Concavity-orientation coding for palmprint recognition

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Efficient feature extraction strategies play an important role in palmprint recognition systems. Among various feature extraction methods, orientation methods such as Competitive Code and Half Orientation Code are the baseline ones. They encode responses of a bank of orientational filters into a binary representation and can match a test palmprint sample in real-time with a relatively good accuracy. However, they use the orientation information based upon this idea that palmprints encompass only straight lines with different orientations, whereas in reality, the majority of palm’s lines are curved. This observation naturally brings the idea that the concavity and orientation features as different aspects of palmprints curves might provide more reliable and discriminative representations in palmprint recognition. Motivated by this idea, in this work we investigate the use of the concavity feature in different orientations for palmprint recognition. The experimental results, which are applied on PolyU II, 2D/3D PolyU, and blue and near infrared range images from Multispectral PolyU palmprint databases prove the efficiency of this idea compared to other coding-based methods.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig 3
Fig 4
Fig 5
Fig 6
Fig 7
Fig. 8

Similar content being viewed by others

References

  1. Altun AA (2013) A combination of genetic algorithm, particle swarm optimization and neural network for palmprint recognition. Neural Comput & Applic 22(1):27–31

    Article  Google Scholar 

  2. Badrinath GS, Gupta P (2011) Stockwell transform based palm-print recognition. Appl Soft Comput 11:4267–4281

    Article  Google Scholar 

  3. Chen GY, Xie WF (2007) Pattern recognition with SVM and dual-tree complex wavelets. Image Vis Comput 25:960–966

    Article  Google Scholar 

  4. Choge AK, Oyama T, Karungaru S, Tsuge S, Fukumi M (2009) A circle based region of interest segmentation method for palmprint recognition. In: Int joint conf of ICROS-SICE. pp 4993–4997

  5. Ding B, Ruan QQ (2006) The Localization of the Palmrprint images based on the maximal effective circle. In: ICSP. 2006

  6. Du F, Yu P, Li H, Zhu L (2011) Palmprint recognition using Gabor feature-based bidirectional 2DLDA. Commun Comput Inf Sci 159(5):230–235

    Google Scholar 

  7. Fei L, Xu Y, Tang W, Zhang D (2016) Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recogn 49:89–101

    Article  Google Scholar 

  8. Fei L, Xu Y, Zhang D (2015) Half-orientation extraction of palmprint features. Pattern Recogn Lett. doi:10.1016/j.patrec.2015.10.003

    Google Scholar 

  9. Guo Z, Zhang D, Zhang L, Zuo W (2009) Palmprint verification using binary orientation co-occurrence vector. Pattern Recogn Lett 30:1219–1227

    Article  Google Scholar 

  10. Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recogn 40(1):339–342

    Article  MATH  Google Scholar 

  11. Jaafar H, Ibrahim S, Ramli DA (2015) A robust and fast computation touchless palm print recognition system using LHEAT and the IFkNCN classifier computational intelligence and neuroscience doi: 10.1155/2015/360217

  12. Jang W, Park D, Lee D, Kim DS (2005) Fingerprint image enhancement based on a half Gabor filter. In: International conference on biometrics. pp 258–264. IEEE

  13. Jazzar M, Muhammad G (2013) Feature selection based verification/identification system using fingerprints and palm print. Arab J Sci Eng 38(4):849–857

    Article  Google Scholar 

  14. Jia W, Huang DS, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recogn 41(5):1504–1513

    Article  MATH  Google Scholar 

  15. 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):2405–2415

    Article  Google Scholar 

  16. Kong WK, Zhang D, Li W. Palmprint feature extraction using 2D Gabor filters. Pattern Recogn 36:2339–2347

  17. Kong AW, Zhang D (2004) Competitive coding scheme for palmprint verification. In: International conference on pattern recognition. pp 520–523. IEEE

  18. Kong A, Zhang D, Kamel M (2006) Palmprint identification using feature-level fusion. Pattern Recogn 39(3):478–487

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  20. Lee TS (1996) Image representation using 2D Gabor wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):1–13

    Google Scholar 

  21. Li W, Xia S, Zhang D, Xu Z (2004) A novel bi-directional matching method of palmprint identification based on of line features. J Comput Res Dev 41(6):996–1002

    Google Scholar 

  22. Li W, Zhang D, Xu Z (2002) Palmprint identification by Fourier transform. Int J Pattern Recognit Artif Intell 16(4):417–432

    Article  Google Scholar 

  23. Lin J, Keogh E, Lonardi S, Chiu B (2003) A symbolic representation of time series, with implications for streaming algorithms In: ACM SIGMOD workshop on research issues in data mining and knowledge discovery. pp 2–11

  24. Lu G, Zhang D, Wang K (2003) Palmprint recognition using eigenpalms features. Pattern Recogn Lett 24(9):1463–1467

    Article  MATH  Google Scholar 

  25. Morales A, Ferrer MA, Kumar A (2011) Towards contactless palmprint authentication. IET Comput Vis 5(6):407–416

    Article  MathSciNet  Google Scholar 

  26. Ni J, Luo J, Liu W (2015) 3D palmprint recognition using Dempster-Shafer fusion theory. J Sens. doi:10.1155/2015/252086

    Google Scholar 

  27. Pan X, Ruan QQ (2008) Palmprint recognition using Gabor feature-based (2D)PCA. Neurocomputing 71:3032–3036

    Article  Google Scholar 

  28. Pan X, Ruan QQ (2009) Palmprint recognition using Gabor-based local invariant features. Neurocomputing 72(7–9):2040–2045

    Article  Google Scholar 

  29. Peters G, Kruger N (1997) Learning object representations by clustering banana wavelet responses. In: International workshop on statistical techniques in pattern recognition. pp 113–118

  30. Poon C, Wong DC, Shen HC (2004) A new method in locating and segmenting palmprint into region of interest. In: Int conf pattern recognition. pp 533–536

  31. Ribaric S, Fratric I (2005) A biometric identification system based on eigenpalm and eigenfinger features. IEEE Trans Pattern Anal Mach Intell 27(11):1698–1709

    Article  Google Scholar 

  32. Sang H, Yuan W, Zhang Z (2009) Research of palmprint recognition based on 2DPCA. In: Advances in neural network. pp 831–838. Lecture Notes in Computer Science Springer-Verlag

  33. Sun Z, Tan T, Wang Y, Li SZ (2005) Ordinal palmprint represention for personal identification. In: IEEE Conf Comput Vis Pattern Recognit. pp 279–284

  34. Tamrakar D, Khanna P (2015) Palmprint verification with XOR-SUM Code. SIViP 9(3):535–542

    Article  Google Scholar 

  35. Wang X, Liang J, Wang M (2013) On-line fast palmprint identification based on adaptive lifting wavelet scheme. Knowl-Based Syst 42:68–73

    Article  Google Scholar 

  36. Wu XQ, Wang KQ, Zhang D (2006) Palmprint texture analysis using derivative of Gaussian filters. In: International conference on computational intelligence and security. IEEE

  37. Wu X, Zhang D, Wang K (2003) Fisherpalms based palmprint recognition. Pattern Recogn Lett 24(15):2829–2838

    Article  Google Scholar 

  38. Yu P, Jie W (2010) An improved method of palmprint recognition based on independent component analysis. J Guangdong Univ Technol 1:014

    Google Scholar 

  39. Zhang D, Kong AW, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050

    Article  Google Scholar 

  40. Zhang Y, Sun D, Qiu Z (2012) Hand-based single sample biometrics recognition. Neural Comput & Applic 21(8):1835–1844

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Abdolmajid Mousavi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tabejamaat, M., Mousavi, A. Concavity-orientation coding for palmprint recognition. Multimed Tools Appl 76, 9387–9403 (2017). https://doi.org/10.1007/s11042-016-3544-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-016-3544-6

Keywords

Navigation