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1887

access icon free Masked SIFT with align-based refinement for contactless palmprint recognition

© The Institution of Engineering and Technology
Received 23/01/2018, Accepted 24/09/2018, Revised 01/07/2018, Published 05/10/2018

Contactless palmprint is considered more user convenient than other biometrics due to its acquisition simplicity and less-private nature. Many challenges arise which affect the performance of common contact-based methods when applied to a contactless environment. For example, pose and illumination variations affect the layout and visibility of palm lines. This study proposes a SIFT-based method with three main modifications from the traditional SIFT. First, the palm regions with no significant lines/wrinkles are masked out to reduce the false features. A region with multi-lines is then described by multi-descriptors rather than a single one. Second, only query and target keypoints with small rotation difference are compared together, instead of comparing them all. This speed-up the comparison and enhance the accuracy, versus SIFT, by reducing the wrong matches. Third, an align-based refinement is applied to filter out the incorrect matches. The method is tested on three contactless hand databases; IITD, GPDS and Sfax-Miracl. It achieves a verification equal error rate of 0.72, 0.84 and 1.14% and a correct identification rate of 98.9, 99 and 98.9% on each database, respectively. These results are significantly better than the state-of-art methods on the same databases by 1.9% for verification and 3.2% for identification.

Inspec keywords: image matching; palmprint recognition; transforms; feature extraction; biometrics (access control)

Other keywords: visibility; query keypoints; traditional SIFT; contactless environment; illumination variations; verification equal error rate; palm lines; palm regions; layout; common contact-based methods; rotation difference; biometrics; less-private nature; comparison process; false features; multidescriptors; align-based refinement; significant lines/wrinkles; masked SIFT; multilines; contactless palmprint recognition; contactless hand databases; main modifications; user convenient; acquisition simplicity

Subjects: Integral transforms; Data security; Computer vision and image processing techniques; Image recognition; Integral transforms

References

    1. 1)
      • 24. Kong, A.W.-K., Zhang, D.: ‘Competitive coding scheme for palmprint verification’. Proc. of the 17th Int. Conf. on Pattern Recognition (ICPR), 2004.
    2. 2)
      • 15. Tamrakar, D., Khanna, P.: ‘Kernel discriminant analysis of block-wise Gaussian derivative phase pattern histogram for palmprint recognition’, J. Vis. Commun. Image Represent., 2016, 40, pp. 432–448.
    3. 3)
      • 11. Ferrer, M.A., Vargas, F., Morales, A.: ‘Bispectral contactless hand based biometric system’. 2nd National Conf. on Telecommunications (CONATEL 2011), Perú, 2011. DB Link: http://www.gpds.ulpgc.es/download/.
    4. 4)
      • 46. Hong, D., Liu, W., Wu, X., et al: ‘Robust palmprint recognition based on the fast variation Vese–Osher model’, Neurocomputing, 2016, 174, (Part B), pp. 999–1012.
    5. 5)
      • 17. Laadjel, M., Al-Maadeed, S., Bouridane, A.: ‘Combining fisher locality preserving projections and passband DCT for efficient palmprint recognition’, Neurocomputing, 2015, 152, pp. 179–189.
    6. 6)
      • 45. Fischler, M., Bolles, R.: ‘Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography’, Commun. ACM, 1981, 24, (6), pp. 381–395.
    7. 7)
      • 27. Jia, W., Huang, D.-S., Zhang, D.: ‘Palmprint verification based on robust line orientation code’, Pattern Recognit., 2008, 41, (5), pp. 1504–1513.
    8. 8)
      • 2. Methani, C.: ‘Camera based palmprint recognition’ (International Institute of Information Technology, Hyderabad, India, 2010).
    9. 9)
      • 26. Zuo, W., Lin, Z., Guo, Z., et al: ‘The multiscale competitive code via sparse representation for palmprint verification’. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 2010.
    10. 10)
      • 16. Hennings-Yeomans, P.H., Kumar, B.V., Savvides, M.: ‘Palmprint classification using multiple advanced correlation filters and palm-specific segmentation’, IEEE Trans. Inf. Forensics Sec., 2007, 2, (3), pp. 613–622.
    11. 11)
      • 22. Al Maadeed, S., Jiang, X., Rida, I., et al: ‘Palmprint identification using sparse and dense hybrid representation’, Multimedia Tools Appl., 2018, 20, pp. 1–15.
    12. 12)
      • 40. Jemaa, S.B., Frikha, M., Moalla, I., et al: ‘Sfax-Miracl hand database for contactless hand biometrics applications’. Proc. of the 5th Intern. Conf. on Image and Signal Processing (ICISP 2012), Marroc, 2012.
    13. 13)
      • 19. Tamrakar, D., Khanna, P.: ‘Noise and rotation invariant RDF descriptor for palmprint identification’, Multimedia Tools Appl., 2016, 75, (10), pp. 5777–5794.
    14. 14)
      • 8. Liu, D., Sun, D.M., Qiu, Z.D.: ‘A novel image enhancement method for SIFT feature extraction of low resolution palmprint images’, Chin. J. Electron., 2011, 20, (1), pp. 111–113.
    15. 15)
      • 23. Rida, I., Al-Maadeed, S., Mahmood, A., et al: ‘Palmprint identification using an ensemble of sparse representations’, IEEE Access, 2018, 6, pp. 3241–3248.
    16. 16)
      • 38. Almaghtuf, J., Khelifi, F.: ‘Self-geometric relationship filter for efficient SIFT key-points matching in full and partial palmprint recognition’, IET Biometrics, 2018, 7, (4), pp. 296–304.
    17. 17)
      • 41. Zhang, J., Tan, T.: ‘Invariant texture segmentation via circular Gabor filter’. Proc. of the 16th IAPR Int. Conf. on Pattern Recognition (ICPR), Quebec, Canada, 2002.
    18. 18)
      • 48. Xu, Y., Fei, L., Zhang, D.: ‘Combining left and right palmprint images for more accurate personal identification’, IEEE Trans. Image Process., 2015, 24, (2), pp. 549–559.
    19. 19)
      • 50. Vedaldi, A.: ‘SIFT for matlab’, The Regents of the University of California, 2006. Available at http://www.cs.ucla.edu/~vedaldi/. Accessed DEC2013.
    20. 20)
      • 1. Michael, G.K.O., Connie, T., Teoh, A.B.J.: ‘Touch-less palm print biometrics: novel design and implementation’, Image Vis. Comput., 2008, 26, (12), pp. 1551–1560.
    21. 21)
      • 37. Jia, W., Zhang, B., Lu, J., et al: ‘Palmprint recognition based on complete direction representation’, IEEE Trans. Image Process., 2017, 26, (9), pp. 4483–4498.
    22. 22)
      • 47. Fei, L., Xu, Y., Zhang, B., et al: ‘Low-rank representation integrated with principal line distance for contactless palmprint recognition’, Neurocomputing, 2016, 218, (19), pp. 264–275.
    23. 23)
      • 5. Leng, L., Li, M., Kim, C., et al: ‘Dual-source discrimination power analysis for multi-instance contactless palmprint recognition’, Multimedia Tools Appl., 2017, 76, (1), pp. 333–354.
    24. 24)
      • 18. Li, H., Wang, L.: ‘Palmprint recognition using dual-tree complex wavelet transform and compressed sensing’. Int. Conf. on Measurement, Information and Control (MIC), 2012.
    25. 25)
      • 10. Kumar, A.: ‘Incorporating cohort information for reliable palmprint authentication’. Proc. ICVGIP, Bhubneshwar, India, 2008. DB Link: http://www4.comp.polyu.edu.hk/~csajaykr/IITD/Database_Palm.htm.
    26. 26)
      • 21. Raghavendra, R., Busch, C.: ‘Novel image fusion scheme based on dependency measure for robust multispectral palmprint recognition’, Pattern Recognit., 2014, 47, (6), pp. 2205–2221.
    27. 27)
      • 6. Zhang, L., Li, L., Yang, A., et al: ‘Towards contactless palmprint recognition: a novel device, a new benchmark, and a collaborative representation based identification approach’, Pattern Recognit., 2017, 69, pp. 199–212.
    28. 28)
      • 12. Sang, H., Yuan, W., Zhang, Z.: ‘Research of palmprint recognition based on 2DPCA’. Int. Symp. on Neural Networks, Berlin, 2009.
    29. 29)
      • 28. Khan, Z., Mian, A., Hu, Y.: ‘Contour code: robust and efficient multispectral palmprint encoding for human recognition’. IEEE Int. Conf. on Computer Vision (ICCV), 2011.
    30. 30)
      • 3. Li, W., Zhang, B., Zhang, L., et al: ‘Principal line-based alignment refinement for palmprint recognition’, IEEE Trans. Syst. Man Cybern. C, Appl. Rev., 2012, 42, (6), pp. 1491–1499.
    31. 31)
      • 25. Sun, Z., Tan, T., Wang, Y., et al: ‘Ordinal palmprint representation for personal identification’. Proc. of the IEEE Computer Society Conf. on Computer Vision and Pattern Recognition, 2005.
    32. 32)
      • 14. Hu, D., Feng, G., Zhou, Z.: ‘Two-dimensional locality preserving projections (2DLPP) with its application to palmprint recognition’, Pattern Recognit., 2007, 40, (1), pp. 339–342.
    33. 33)
      • 7. ELSayed, A.S., Ebeid, H.M., Roushdy, M., et al: ‘A method for contactless palm ROI extraction’. 11th Int. Conf. on Computer Engineering & Systems (ICCES), Egypt, 2017.
    34. 34)
      • 39. Lowe, D.G.: ‘Distinctive image features from scale-invariant keypoints’, Int. J. Comput. Vis., 2004, 60, (2), pp. 91–110.
    35. 35)
      • 34. Morales, A., Ferrer, M.A., Kumar, A.: ‘Towards contactless palmprint authentication’, IET Comput. Vis., 2011, 5, (6), pp. 407–416.
    36. 36)
      • 32. Fei, L., Xu, Y., Tang, W., et al: ‘Double-orientation code and nonlinear matching scheme for palmprint recognition’, Pattern Recognit., 2016, 49, pp. 89–101.
    37. 37)
      • 29. Zhang, L., Li, H., Niu, J.: ‘Fragile bits in palmprint recognition’, IEEE Signal Process. Lett., 2012, 19, (10), pp. 663–666.
    38. 38)
      • 42. Zuiderveld, K.: ‘Contrast limited adaptive histogram equalization’, in Heckbert, P.S. (Ed.) ‘Graphics gems IV’ (Academic Press Professional, Inc., San Diego, CA, USA, 1994), pp. 474–485.
    39. 39)
      • 20. Xu, Y., Fan, Z., Qiu, M., et al: ‘A sparse representation method of bimodal biometrics and palmprint recognition experiments’, Neurocomputing, 2013, 103, pp. 164–171.
    40. 40)
      • 36. Fei, L., Wen, J., Zhang, Z., et al: ‘Local multiple directional pattern of palmprint image’. 23rd Int. Conf. on Pattern Recognition (ICPR), Mexico, 2016.
    41. 41)
      • 9. Zhang, K., Huang, D., Zhang, D.: ‘An optimized palmprint recognition approach based on image sharpness’, Pattern Recognit. Lett., 2017, 85, pp. 65–71.
    42. 42)
      • 35. Luo, Y.-T., Zhao, L.-Y., Zhang, B., et al: ‘Local line directional pattern for palmprint recognition’, Pattern Recognit., 2016, 50, pp. 26–44.
    43. 43)
      • 4. Wu, X., Zhao, Q., Bu, W.: ‘A SIFT-based contactless palmprint verification approach using iterative RANSAC and local palmprint descriptors’, Pattern Recognit., 2014, 47, (10), pp. 3314–3326.
    44. 44)
      • 31. Zheng, Q., Kumar, A., Pan, G.: ‘A 3d feature descriptor recovered from a single 2d palmprint image’, IEEE Trans. Pattern Anal. Mach. Intell., 2016, 38, (6), pp. 1272–1279.
    45. 45)
      • 49. Dubitzky, W., Granzow, M., Berrar, D.P.: ‘Fundamentals of data mining in genomics and proteomics’ (Springer Science & Business Media, USA, 2007).
    46. 46)
      • 30. Fei, L., Xu, Y., Zhang, D.: ‘Half-orientation extraction of palmprint features’, Pattern Recognit. Lett., 2016, 69, pp. 35–41.
    47. 47)
      • 13. Wang, M., Ruan, Q.: ‘Palmprint recognition based on two-dimensional methods’. Int. Conf. on Signal Processing, 2006.
    48. 48)
      • 33. Qian, J., Yang, J., Gao, G.: ‘Discriminative histograms of local dominant orientation (D-HLDO) for biometric image feature extraction’, Pattern Recognit., 2013, 46, (10), pp. 2724–2739.
    49. 49)
      • 44. ELSayed, A.S., Nagaty, K., ELArif, T.: ‘An enhanced histogram matching approach using the retinal filter's compression function for illumination normalization in face recognition’. Int. Conf. Image Analysis and Recognition, Portugal, 2008.
    50. 50)
      • 43. Zhou, Y., Kumar, A.: ‘Contactless palm vein identification using multiple representations’. Fourth IEEE Int. Conf. on Biometrics: Theory Applications and Systems (BTAS), 2010.
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