access icon free Palm vein recognition scheme based on an adaptive Gabor filter

We propose a novel palm vein recognition scheme based on an adaptive 2D Gabor filter. Three key steps were studied in this scheme: region of interest (ROI) extraction, adaptive Gabor filtering, and template matching. First, in the palm vein image extraction step, the authors used the index finger on both sides of the valley to locate the square area, and then iteratively expanded the area of the square box to maximise the ROI. Second, in the feature extraction step, a novel parameter selection scheme was proposed for optimising the Gabor filter. Third, in the template matching step, the author presented a novel template matching algorithm referred to as the minimum normalised Hamming distance. Experimental results demonstrated that the scheme achieved good performance with an EER of 0.12%.

Inspec keywords: vein recognition; feature extraction; feature selection; image segmentation; Gabor filters; image matching; adaptive filters

Other keywords: region of interest extraction; minimum normalised Hamming distance; feature extraction; template matching; palm vein recognition; index finger; ROI extraction; adaptive 2D Gabor filtering; Gabor filter optimisation; palm vein image extraction; parameter selection

Subjects: Image recognition; Computer vision and image processing techniques; Filtering methods in signal processing

References

    1. 1)
      • 8. Lee, J.-C.: ‘A novel biometric system based on palm vein image’, Pattern Recognit. Lett., 2012, 33, (12), pp. 15201528.
    2. 2)
      • 24. Zhang, D., Guo, Z., Lu, G., et al: ‘Online joint palmprint and palmvein verification’, Expert Syst. Appl., 2011, 38, (3), pp. 26212631.
    3. 3)
      • 14. Mehrotra, R., Namuduri, K.R., Ranganathan, N.: ‘Gabor filter-based edge detection’, Pattern Recogn., 1992, 25, (12), pp. 14791494.
    4. 4)
      • 9. Li, X., Guo, S., Gao, F., et al: ‘Vein pattern recognitions by moment invariants’. Int. Conf. on Bioinformatics & Biomedical Engineering, Wuhan, China, July 2007, pp. 612615.
    5. 5)
      • 23. Yan, X.K., Kang, W.X., Deng, F.Q., et al: ‘Palm vein recognition based on multi-sampling and feature-level fusion’, Neurocomputing, 2015, 151, (151), pp. 798807.
    6. 6)
      • 5. Song, W., Kim, T., Kim, H.C., et al: ‘A finger-vein verification system using mean curvature’, Pattern Recognit. Lett., 2011, 32, (11), pp. 15411547.
    7. 7)
      • 1. Lee, J.C., Lee, C.H., Hsu, C.B., et al: ‘Dorsal hand vein recognition based on 2D Gabor filters’, Imaging Sci. J., 2014, 62, (3), pp. 127138.
    8. 8)
      • 7. Chen, J., Shan, S., He, C., et al: ‘WLD: a robust local image descriptor’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 32, (9), pp. 17051720.
    9. 9)
      • 6. Mirmohamadsadeghi, L., Drygajlo, A.: ‘Palm vein recognition with local binary patterns and local derivative patterns’. Int. Joint Conf. on Biometrics, Washington, USA, October 2011, pp. 16.
    10. 10)
      • 21. Wang, L., Leedham, G., Siu-Yeung Cho, D.: ‘Minutiae feature analysis for infrared hand vein pattern biometrics’, Pattern Recogn., 2008, 41, (3), pp. 920929.
    11. 11)
      • 11. Liu, Z., Yin, Y., Wang, H., et al: ‘Finger vein recognition with manifold learning’, J. Netw. Comput. Appl., 2010, 33, (3), pp. 275282.
    12. 12)
      • 15. Han, W.Y., Lee, J.C.: ‘Palm vein recognition using adaptive Gabor filter’, Expert Syst. Appl., 2012, 39, (18), pp. 1322513234.
    13. 13)
      • 22. Wang, J.-G., Yau, W.-Y., Suwandy, A., et al: ‘Person recognition by fusing palmprint and palm vein images based on ‘Laplacianpalm’ representation’, Pattern Recogn., 2008, 41, (5), pp. 15141527.
    14. 14)
      • 2. Lin, C.L., Fan, K.C.: ‘Biometric verification using thermal images of palm-dorsa vein patterns’, IEEE Trans. Circuits Syst. Video Technol., 2004, 14, (2), pp. 199213.
    15. 15)
      • 12. Yang, G., Xi, X., Yin, Y.: ‘Finger vein recognition based on (2D)(2) PCA and metric learning’, J. Biomed. Biotechnol., 2012, 2012, (3), p. 324249.
    16. 16)
      • 16. Jia, W., Huang, D.-S., Zhang, D.: ‘Palmprint verification based on robust line orientation code’, Pattern Recogn., 2008, 41, (5), pp. 15041513.
    17. 17)
      • 19. ‘CASIA-MS-PalmprintV1’, http://biometrics.idealtest.org/, accessed 20 January 2016.
    18. 18)
      • 18. Yue, F., Zuo, W., Zhang, D., et al: ‘Orientation selection using modified FCM for competitive code-based palmprint recognition’, Pattern Recogn., 2009, 42, (11), pp. 28412849.
    19. 19)
      • 4. Zhou, Y., Kumar, A.: ‘Human identification using palm-vein images’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (4), pp. 12591274.
    20. 20)
      • 17. Zhou, Y.J., Liu, Y.Q., Feng, Q.J., et al: ‘Palm-vein classification based on principal orientation features’, PLoS One, 2014, 9, (11), p. 12.
    21. 21)
      • 13. Kang, W.X., Wu, Q.X.: ‘Contactless palm vein recognition using a mutual foreground-based local binary pattern’, IEEE Trans. Inf. Forensics Sec., 2014, 9, (11), pp. 19741985.
    22. 22)
      • 20. Wang, J.-G., Yau, W.-Y., Suwandy, A., et al: ‘Fusion of palmprint and palm vein images for person recognition based on ‘Laplacianpalm’ feature’. 2007 IEEE Conf. on Computer Vision and Pattern Recognition, Minneapolis, USA, June 2007, pp. 18.
    23. 23)
      • 10. Kang, W.X., Liu, Y., Wu, Q.X., et al: ‘Contact-free palm-vein recognition based on local invariant features’, PLoS One, 2014, 9, (5), pp. 12391245.
    24. 24)
      • 3. Cross, J.M., Smith, C.L.: ‘Thermographic imaging of the subcutaneous vascular network of the back of the hand for biometric identification’. Int. Carnahan Conf. on Security Technology, Sanderstead, UK, October 1995, pp. 2035.
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