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Personal identification from degraded and incomplete high resolution palmprints

Personal identification from degraded and incomplete high resolution palmprints

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A high resolution palmprint recognition system using full or partial, eventually degraded, palmprints is presented. Previous work on palmprint matching addressed mostly commercial applications, using low resolution images. However, in forensic scenarios, high resolution palmprints, although incomplete and/or degraded, are often used. Degradations may result from surface irregularities or impurities, which are often modelled as Gaussian or salt and pepper noise, as well as smearing of the palmprint because of hand sliding, which in this work is modelled as motion blur. The proposed system matches palmprints, full or partial, undegraded or subjected to one of the above degradations, against palmprints registered in a database. The proposed system extends previous work of the authors by adaptively selecting between two palmprint matching approaches, achieving better recognition results than either of the two individual strategies. The first approach relies on a motion blur compensation technique, while the second is based on a combination of the Fourier–Mellin transform with a modified phase-only correlation matching strategy. The presented results show that for sufficiently large palmprint areas the blur compensation technique works better, while for small-sized partial palmprints with large motion blur degradation values the second approach based on correlation is preferred.

References

    1. 1)
      • 1. On latent palmprint matching’, http://www.cse.msu.edu/biometrics/Publications/Palmprints/JainDemirkusOnLatentPalmprintMatching08.pdf, accessed January 2015.
        .
    2. 2)
    3. 3)
      • M. Laadjel , F. Kurugollu , A. Bouridane , S. Boussakta .
        3. Laadjel, M., Kurugollu, F., Bouridane, A., Boussakta, S.: ‘Degraded partial palmprint recognition for forensic investigations’. Int. Conf. Image Processing, Cairo, Egypt, November 2009, pp. 15131516.
        . Int. Conf. Image Processing , 1513 - 1516
    4. 4)
    5. 5)
      • R. Wang , D. Ramos , J. Fierrez , R.P. Krish .
        5. Wang, R., Ramos, D., Fierrez, J., Krish, R.P.: ‘Towards regional fusion for high-resolution palmprint recognition’. Conf. Graphics, Patterns Images, Arequipa, Peru, August 2013, pp. 357361.
        . Conf. Graphics, Patterns Images , 357 - 361
    6. 6)
    7. 7)
      • L. Carreira , P.L. Correia , L.D. Soares .
        7. Carreira, L., Correia, P.L., Soares, L.D.: ‘On high resolution palmprint matching’. Int. Workshop Biometrics Forensics, Valletta, Malta, March 2014, pp. 16.
        . Int. Workshop Biometrics Forensics , 1 - 6
    8. 8)
      • S. Singh , P.L. Correia , L.D. Soares .
        8. Singh, S., Correia, P.L., Soares, L.D.: ‘Improved rotation-invariant degraded partial palmprint recognition technique’. European Signal Processing Conf., Bucharest, Romania, August 2012, pp. 14991503.
        . European Signal Processing Conf. , 1499 - 1503
    9. 9)
      • F. Brusius , U. Schwanecke , P. Barth . (2013)
        9. Brusius, F., Schwanecke, U., Barth, P.: ‘Blind image deconvolution of linear motion blur’, in Csurka, G., et al (Eds.): ‘VISIGRAPP 2011, CCIS 274’ (Springer, 2013, 1st edn.), pp. 105119.
        .
    10. 10)
      • 10. THUPALMLAB’, http://ivg.au.tsinghua.edu.cn/index.php?n=Data.Tsinghua500ppi, accessed July 2013.
        .
    11. 11)
      • 11. PV-TEST’, https://biolab.csr.unibo.it/fvcongoing/UI/Form/Home.aspx, accessed March 2012.
        .
    12. 12)
    13. 13)
      • R.C. Gonzalez , R.E. Woods . (2007)
        13. Gonzalez, R.C., Woods, R.E.: ‘Image restoration and reconstruction’, in ‘Digital image processing’ (Prentice Hall, Upper Saddle River, NJ, 2007, 3rd edn.), pp. 352357.
        .
    14. 14)
      • 14. Steerable-filter’, https://github.com/andreydung/Steerable-filter, accessed February 2013.
        .
    15. 15)
      • L. Juan , O. Gwun .
        15. Juan, L., Gwun, O.: ‘A comparison of SIFT, PCA-SIFT and SURF’, Int. J. Image Process., 2009, 3, (4), pp. 143152.
        . Int. J. Image Process. , 4 , 143 - 152
    16. 16)
      • 16. VLFeat toolkit’, http://www.vlfeat.org/index.html, accessed February 2013.
        .
    17. 17)
      • R.C. Gonzalez , R.E. Woods . (2007)
        17. Gonzalez, R.C., Woods, R.E.: ‘Regional descriptors’, in ‘Digital image processing’ (Prentice Hall, Upper Saddle River, NJ, 2007, 3rd edn.), pp. 849861.
        .
    18. 18)
    19. 19)
    20. 20)
      • 20. SourceAFIS’, http://www.sourceafis.org/blog/, accessed January 2015.
        .
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