Advertisement

Pattern Analysis and Applications

, Volume 21, Issue 1, pp 261–275 | Cite as

Generalized Gabor filters for palmprint recognition

  • Mohsen Tabejamaat
  • Abdolmajid Mousavi
Industrial and Commercial Application

Abstract

Orientation-based coding approaches have recently been widely employed for face and palmprint recognition where generally, one starts with a set of Gabor filters to extract orientation information and then proceeds to code dominant orientations as features for each point of the palmprint. However, as the Gabor filter is developed to model two-dimensional receptive fields of simple cells in straits cortex, it might not be our best choice when dealing with curved and complex structures inherent in the palmprint texture. Motivated by this intuition, this paper shows that Gabor filters are a subset of a bigger family of filters which we refer to as generalized Gabor filter (GGF). Depending on the values of its parameters, a GGF takes a rather diverse shapes and orientations, which results in a potentially finer feature extraction capability. We show this improved capability by employing GGFs in the palmprint verification process. In applying our method, two different sub-banks of GGFs are defined for the orientation-based feature extraction of palmprints, and when compared with Gabor filters, it will be shown that GGFs have the upper hand in capturing orientation features. Furthermore, compared with the competitive code—one of the well-known orientation-based coding methods—the number of employed orientations is reduced to half. This would automatically compensate for a double usage of the filter banks, which otherwise could increase the time complexity of using GGFs. These ideas are further elaborated using a set of experiments on PolyU II and PolyU 2D/3D palmprint databases. The results show the preeminence of using GGFs both in terms of accuracy and efficiency.

Keywords

Palmprint recognition Pattern recognition Gabor filter Generalized Gabor filter 

References

  1. 1.
    Zhang D, Zuo W, Yue F (2012) A comparative study of palmprint recognition algorithms. ACM Comput Surv 44(1):2CrossRefGoogle Scholar
  2. 2.
    Lu G, Zhang D, Wang K (2003) Palmprint recognition using Eigenpalm features. Pattern Recognit Lett 24(9):1463–1467CrossRefMATHGoogle Scholar
  3. 3.
    Xu Y, Zhang D, Yang JY (2010) A feature extraction method for use with bimodal biometrics. Pattern Recognit 43(3):1106–1115CrossRefMATHGoogle Scholar
  4. 4.
    Xu Y, Yang JY, Jin Z (2004) A novel method for fisher discriminant analysis. Pattern Recognit 37(2):381–384CrossRefMATHGoogle Scholar
  5. 5.
    Zhang Y, Sun D, Qiu Z (2012) Hand-based single sample biometrics recognition. Neural Comput Appl 21(8):1835–1844CrossRefGoogle Scholar
  6. 6.
    Kokiopoulou E, Saad Y (2007) Orthogonal neighborhood preserving projections: a projection-based dimensionality reduction technique. IEEE Trans Pattern Anal Mach Intell 29(12):2143–2156CrossRefGoogle Scholar
  7. 7.
    He X, Cai D, Yan S, Zhang HJ (2005) Neighborhood preserving embedding. In: International conference on computer visio (ICCV), pp 1208–1213Google Scholar
  8. 8.
    Wang Y, Wu Y (2010) Complete neighborhood preserving embedding for face recognition. Pattern Recognit 43(3):1008–1015CrossRefMATHGoogle Scholar
  9. 9.
    Sang H, Yuan W, Zhang Z (2009) Research of palmprint recognition based on 2DPCA. In: International symp on neural networks (ISNN), pp 831–838Google Scholar
  10. 10.
    Li M, Yuan B (2005) 2D-LDA: a statistical linear discriminant analysis for image matrix. Pattern Recognit Lett 26(5):527–532CrossRefGoogle Scholar
  11. 11.
    Hu D, Feng G, Zhou Z (2007) Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition. Pattern Recognit 40(1):339–342CrossRefMATHGoogle Scholar
  12. 12.
    Zhang H, Wu QJ, Chow TW, Zhao M (2012) A two-dimensional Neighborhood Preserving Projection for appearance-based face recognition. Pattern Recognit 45(5):1866–1876CrossRefMATHGoogle Scholar
  13. 13.
    Du H, Wang S, Zhao J, Xu N (2010) Two-dimensional neighborhood preserving embedding for face recognition. In: Information management and engineering (ICIME), pp 500–504Google Scholar
  14. 14.
    Wang Y, Xie JB, Wu Y (2014) Two-dimensional complete neighborhood preserving embedding. Neural Comput and Appl 24(7–8):1505–1517CrossRefGoogle Scholar
  15. 15.
    Nibouche O, Jiang J, Trundle P (2012) Analysis of performance of palmprint matching with enforced sparsity. Digital Signal Process 22(2):348–355MathSciNetCrossRefGoogle Scholar
  16. 16.
    Guo XM, Wan LM, Wang CY (2014) A palmprint recognition based on collaborative representation. In: Applied mechanics and materials, pp 1317–1322Google Scholar
  17. 17.
    Wei L, Xu F, Yin J, Wu A (2014) Kernel locality-constrained collaborative representation based discriminant analysis. Knowl Syst 40:212–220CrossRefGoogle Scholar
  18. 18.
    Kong A, Zhang D (2004) Competitive coding scheme for palmprint verification. In: International conference on pattern recognit, pp 520–523Google Scholar
  19. 19.
    Duta N, Jain A, Mardia K (2002) Matching of palmprints. Pattern Recognit Lett 23(4):477–485CrossRefMATHGoogle Scholar
  20. 20.
    Han C, Cheng H, Lin C, Fan K (2003) Personal authentication using palm-print features. Pattern Recognit 36(2):371–381CrossRefGoogle Scholar
  21. 21.
    Lin S, Yuan WQ, Wu W, Fang T (2012) Blurred palmprint recognition based on DCT and block energy of principal line. J Optoelectron Laser 23(11):2200–2206Google Scholar
  22. 22.
    Dai J, Feng J, Zhou J (2012) Robust and efficient ridge-based palmprint matching. IEEE Trans Pattern Anal Mach Intell 34(8):1618–1632CrossRefGoogle Scholar
  23. 23.
    Liu E, Jain AK, Tian J (2013) A coarse to fine minutiae-based latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 35:2307–2322CrossRefGoogle Scholar
  24. 24.
    Chen F, Huang X, Zhou J (2013) Hierarchical minutiae matching for fingerprint and palmprint identification. IEEE Trans Image Process 22(12):4964–4971MathSciNetCrossRefMATHGoogle Scholar
  25. 25.
    Zhang D, Kong WK, You J, Wong M (2003) Online palmprint identification. IEEE Trans Pattern Anal Mach Intell 25(9):1041–1050CrossRefGoogle Scholar
  26. 26.
    Kong A, Zhang D, Kamel M (2006) Palmprint identification using feature-level fusion. Pattern Recognit 39(3):478–487CrossRefMATHGoogle Scholar
  27. 27.
    Jia W, Huang DS, Zhang D (2008) Palmprint verification based on robust line orientation code. Pattern Recognit 41(5):1504–1513CrossRefMATHGoogle Scholar
  28. 28.
    Yue F, Zuo W, Zhang D, Wang K (2009) Orientation selection using modified FCM for competitive code-based palmprint recognition. Pattern recognit 42(11):2841–2849CrossRefMATHGoogle Scholar
  29. 29.
    TamrakarD Khanna P (2015) Palmprint verification with XOR-SUM Code. Signal image and video process 9(3):535–542CrossRefGoogle Scholar
  30. 30.
    Sun Z, Tan T, Wang Y, Li SZ (2005) Ordinal palmprint representation for personal identification. In: Computer vision and pattern recognitio (CVPR), pp 279–284Google Scholar
  31. 31.
    Wu X, Wang K, Zhang D (2006) Palmprint texture analysis using derivative of Gaussian filters. In: Computational intelligence and security, pp 751–754Google Scholar
  32. 32.
    Fei L, Xu Y, Tang W, Zhang D (2016) Double-orientation code and nonlinear matching scheme for palmprint recognition. Pattern Recognit 49:89–101CrossRefGoogle Scholar
  33. 33.
    Fei L, Xu Y, Zhang D (2016) Half-orientation extraction of palmprint features. Pattern Recognit Lett 69:35–41CrossRefGoogle Scholar
  34. 34.
    Guo Z, Zhang D, Zhang L, Zuo W (2009) Palmprint verification using binary orientation co-occurrence vector. Pattern Recognit Lett 30(13):1219–1227CrossRefGoogle Scholar
  35. 35.
    Zhang L, Li H, Niu J (2012) Fragile bits in palmprint recognition. IEEE Signal Process Lett 19(10):663–666CrossRefGoogle Scholar
  36. 36.
    Zuo W, Lin Z, Guo Z, Zhang D (2010) The multiscale competitive code via sparse representation for palmprint verification. In: Computer vision and pattern recognition (CVPR), pp 2265–2272Google Scholar
  37. 37.
    Tamrakar D, Khanna P (2011) Palmprint verification using competitive index with PCA. In: Signal Processing, Communication, Computing and Networking Technologies (ICSCCN), pp 768–771Google Scholar
  38. 38.
    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 Recognit 50:26–44CrossRefGoogle Scholar
  39. 39.
    Yue F, Zuo W, Wang K, Zhang D (2008) A performance evaluation of filter design and coding schemes for palmprint recognition. In: International conference on pattern recognition (ICPR), pp 1–4Google Scholar
  40. 40.
    Pan X, Ruan QQ (2008) Palmprint recognition using Gabor feature-based (2D)2PCA. Neurocomputing 71(13):3032–3036CrossRefGoogle Scholar
  41. 41.
    Cui L, Wei Y, Tang YY, Li H (2010) Gabor-based tensor local discriminant embedding and its application on palmprint recognition. Int J Wavel Multiresolution Inf Process 8(2):327–342MathSciNetCrossRefMATHGoogle Scholar
  42. 42.
    Gabor D (1946) Theory of communication. J Institut Electric Eng 93:429–457Google Scholar
  43. 43.
    Daugman J (1980) Two-dimensional spectral analysis of cortical receptive field profiles. Vis Res 20(10):847–856CrossRefGoogle Scholar
  44. 44.
    Lee TS (1996) Image Representation Using 2D Gabor Wavelets. IEEE Trans Pattern Anal Mach Intell 18(10):1–13Google Scholar
  45. 45.
    Fei L, Zhang B, Xu Y, Yan L (2016) Palmprint recognition using neighboring direction indicator. IEEE Trans Human Mach Syst 46(6):787–798CrossRefGoogle Scholar
  46. 46.
    Gradshteyn IS, Ryzhik IM (2007) Table of Integrals, Series, and Products. 7th edition, pp 365Google Scholar

Copyright information

© Springer-Verlag London Ltd. 2017

Authors and Affiliations

  1. 1.Department of Electronic Engineering, Faculty of EngineeringLorestan UniversityKhorramabadIran

Personalised recommendations