access icon free Perfect fingerprint orientation fields by locally adaptive global models

Fingerprint recognition is widely used for verification and identification in many commercial, governmental and forensic applications. The orientation field (OF) plays an important role at various processing stages in fingerprint recognition systems. OFs are used for image enhancement, fingerprint alignment, for fingerprint liveness detection, fingerprint alteration detection and fingerprint matching. In this study, a novel approach is presented to globally model an OF combined with locally adaptive methods. The authors show that this model adapts perfectly to the ‘true OF’ in the limit. This perfect OF is described by a small number of parameters with straightforward geometric interpretation. Applications are manifold: Quick expert marking of very poor quality (for instance latent) OFs, high-fidelity low parameter OF compression and a direct road to ground truth OFs markings for large databases, say. In this contribution, they describe an algorithm to perfectly estimate OF parameters automatically or semi-automatically, depending on image quality, and they establish the main underlying claim of high-fidelity low parameter OF compression.

Inspec keywords: image coding; image matching; image enhancement; data compression; fingerprint identification

Other keywords: fingerprint matching; perfect fingerprint orientation fields; geometric interpretation; image enhancement; fingerprint alignment; ground truth OF; adaptive global models; fingerprint liveness detection; fingerprint recognition; fingerprint alteration detection

Subjects: Image and video coding; Computer vision and image processing techniques; Data handling techniques; Image recognition

References

    1. 1)
      • 52. Yoon, S., Feng, J., Jain, A.K.: ‘On latent fingerprint enhancement’. Proc. BTHI, Orlando, FL, USA, April 2010, pp. 110.
    2. 2)
      • 5. Chikkerur, S., Cartwright, A., Govindaraju, V.: ‘Fingerprint image enhancement using STFT analysis’, Pattern Recognit., 2007, 40, (1), pp. 198211.
    3. 3)
      • 42. Feng, J., Zhou, J., Jain, A.K.: ‘Orientation field estimation for latent fingerprint enhancement’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, (4), pp. 925940.
    4. 4)
      • 13. Yoon, S., Feng, J., Jain, A.K.: ‘Altered fingerprints: analysis and detection’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (3), pp. 451464.
    5. 5)
      • 23. Galar, M., Derrac, J., Peralta, D., et al: ‘A survey of fingerprint classification part I: taxonomies on feature extraction methods and learning models’, Knowl.-Based Syst., 2015, 81, pp. 7697.
    6. 6)
      • 32. Feng, J., Shi, Y., Zhou, J.: ‘Robust and efficient algorithms for separating latent overlapped fingerprints’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (5), pp. 14981510.
    7. 7)
      • 11. Gottschlich, C., Mikaelyan, A., Olsen, M.A., et al: ‘Improving fingerprint alteration detection’. Proc. ISPA, Zagreb, Croatia, September 2015, pp. 8588.
    8. 8)
      • 49. Oehlmann, L., Huckemann, S., Gottschlich, C.: ‘Performance evaluation of fingerprint orientation field reconstruction methods’. Proc. IWBF, Gjovik, Norway, March 2015, pp. 16.
    9. 9)
      • 50. Thai, D.H., Gottschlich, C.: ‘Global variational method for fingerprint segmentation by three-part decomposition’, IET Biometrics, 2016, 5, (2), pp. 120130.
    10. 10)
      • 34. Hildebrandt, M., Dittmann, J.: ‘StirTraceV2.0: enhanced benchmarking and tuning of printed fingerprint detection’, IEEE Trans. Inf. Forensics Sec., 2015, 10, (4), pp. 833848.
    11. 11)
      • 7. Ghafoor, M., Taj, I.A., Ahmad, W., et al: ‘Efficient 2-fold contextual filtering approach for fingerprint enhancement’, IET Image Process., 2014, 8, (7), pp. 417425.
    12. 12)
      • 4. Gottschlich, C.: ‘Curved-region-based ridge frequency estimation and curved Gabor filters for fingerprint image enhancement’, IEEE Trans. Image Process., 2012, 21, (4), pp. 22202227.
    13. 13)
      • 14. Ellingsgaard, J., Sousedik, C., Busch, C.: ‘Detecting fingerprint alterations by orientation field and minutiae orientation analysis’. Proc. IWBF, Valletta, Malta, March 2014, pp. 16.
    14. 14)
      • 51. Thai, D.H., Gottschlich, C.: ‘Directional global three-part image decomposition’, EURASIP J. Image Video Process., 2016, 2016, (12), pp. 120.
    15. 15)
      • 46. Ram, S., Bischof, H., Birchbauer, J.: ‘Modelling fingerprint ridge orientation using Legendre polynomials’, Pattern Recognit., 2010, 43, (1), pp. 342357.
    16. 16)
      • 20. Tams, B.: ‘Cryptanalysis of the fuzzy vault for fingerprints: vulnerabilities and countermeasures’. PhD thesis, University of Goettingen, Goettingen, Germany, December 2012.
    17. 17)
      • 56. Shao, G., Wu, Y., Yong, A., et al: ‘Fingerprint compression based on sparse representation’, IEEE Trans. Image Process., 2014, 23, (2), pp. 489501.
    18. 18)
      • 21. Tams, B.: ‘Unlinkable minutiae-based fuzzy vault for multiple fingerprints’, IET Biometrics, 2016, 5, (3), p. 170180.
    19. 19)
      • 16. Gottschlich, C.: ‘Fingerprint growth prediction, image preprocessing and multi-level judgment aggregation’. PhD thesis, University of Goettingen, Goettingen, Germany, April 2010.
    20. 20)
      • 18. Krish, R.P., Fierrez, J., Ramos, D., et al: ‘Pre-registration of latent fingerprints based on orientation field’, IET Biometrics, 2015, 4, (2), pp. 4252.
    21. 21)
      • 44. Turroni, F., Maltoni, D., Cappelli, R., et al: ‘Improving fingerprint orientation extraction’, IEEE Trans. Inf. Forensics Sec., 2011, 6, (3), pp. 10021013.
    22. 22)
      • 22. Cappelli, R., Lumini, A., Maio, D., et al: ‘Fingerprint classification by directional image partitioning’, IEEE Transactions on Pattern Analysis and Machine Intelligence, 1999, 21, (5), pp. 402421.
    23. 23)
      • 27. Imdahl, C., Huckemann, S., Gottschlich, C.: ‘Towards generating realistic synthetic fingerprint images’. Proc. ISPA, Zagreb, Croatia, September 2015, pp. 8084.
    24. 24)
      • 10. Sousedik, C., Busch, C.: ‘Presentation attack detection methods for fingerprint recognition systems: a survey’, IET Biometrics, 2014, 3, (4), pp. 219233.
    25. 25)
      • 15. Tico, M., Kuosmanen, P.: ‘Fingerprint matching using an orientation-based minutia descriptor’, IEEE Trans. Pattern Anal. Mach. Intell., 2003, 25, (8), pp. 10091014.
    26. 26)
      • 37. Bazen, A.M., Gerez, S.H.: ‘Systematic methods for the computation of the directional fields and singular points of fingerprints’, IEEE Trans. Pattern Anal. Mach. Intell., 2002, 24, (7), pp. 905919.
    27. 27)
      • 41. Eltzner, B., Wollnik, C., Gottschlich, C., et al: ‘The filament sensor for near real-time detection of cytoskeletal fiber structures’, PLoS ONE, 2015, 10, (5), p. e0126346.
    28. 28)
      • 54. Thärnå, J., Nilsson, K., Bigun, J.: ‘Orientation scanning to improve lossless compression of fingerprint images’. Proc. AVBPA, Guildford, UK, June 2003, pp. 343350.
    29. 29)
      • 2. Thai, D.H., Huckemann, S., Gottschlich, C.: ‘Filter design and performance evaluation for fingerprint image segmentation’, PLoS ONE, 2016, 11, (5), p. e0154160.
    30. 30)
      • 12. Bigun, J., Mikaelyan, A.: ‘Dense frequency maps by structure tensor and logarithmic scale space: application to forensic fingerprints’, 2015, http://www.diva-portal.org/smash/get/diva2:810855/FULLTEXT01.pdf.
    31. 31)
      • 35. Bigun, J., Granlund, G.H.: ‘Optimal orientation detection of linear symmetry’. Proc. ICCV, London, UK, June 1987, pp. 433438.
    32. 32)
      • 33. Zhao, Q., Jain, A.K.: ‘Model based separation of overlapping latent fingerprints’, IEEE Trans. Inf. Forensics Sec., 2012, 7, (3), pp. 904918.
    33. 33)
      • 29. Vizcaya, P.R., Gerhardt, L.A.: ‘A nonlinear orientation model for global description of fingerprints’, Pattern Recognit., 1996, 29, (7), pp. 12211231.
    34. 34)
      • 17. Yager, N., Amin, A.: ‘Evaluation of fingerprint orientation field registration algorithms’. Proc. ICPR, Cambridge, UK, August 2004.
    35. 35)
      • 24. Cappelli, R., Erol, A., Maio, D., et al: ‘Synthetic fingerprint-image generation’. Proc. 15th Int. Conf. Pattern Recognition, ICPR, Barcelona, Spain, September 2000, pp. 37.
    36. 36)
      • 30. Gottschlich, C., Mihăilescu, P., Munk, A.: ‘Robust orientation field estimation and extrapolation using semilocal line sensors’, IEEE Trans. Inf. Forensics Sec., 2009, 4, (4), pp. 802811.
    37. 37)
      • 26. Kücken, M., Champod, C.: ‘Merkel cells and the individuality of friction ridge skin’, J. Theor. Biol., 2013, 317, pp. 229237.
    38. 38)
      • 6. Bartůněk, J.S., Nilsson, M., Sällberg, B., et al: ‘Adaptive fingerprint image enhancement with emphasis on preprocessing of data’, IEEE Trans. Image Process., 2013, 22, (2), pp. 644656.
    39. 39)
      • 3. Gottschlich, C., Schönlieb, C.-B.: ‘Oriented diffusion filtering for enhancing low-quality fingerprint images’, IET Biometrics, 2012, 1, (2), pp. 105113.
    40. 40)
      • 40. Gottschlich, C., Mihăilescu, P., Munk, A.: ‘Robust orientation field estimation in fingerprint images with broken ridge lines’. Proc. ISPA, Salzburg, Austria, September 2009, pp. 529533.
    41. 41)
      • 55. Larkin, K.G., Fletcher, P.A.: ‘A coherent framework for fingerprint analysis: are fingerprints holograms?’, Opt. Express, 2007, 15, (14), pp. 86678677.
    42. 42)
      • 1. Maltoni, D., Maio, D., Jain, A.K., et al: ‘Handbook of fingerprint recognition’ (Springer, London, UK, 2009).
    43. 43)
      • 38. Bigun, J.: ‘Vision with direction’ (Springer, Berlin, Germany, 2006).
    44. 44)
      • 47. Cappelli, R., Maio, D., Maltoni, D.: ‘Semi-automatic enhancement of very low quality fingerprints’. Proc. ISPA, Salzburg, Austria, September 2009, pp. 678683.
    45. 45)
      • 36. Bigun, J.: ‘Recognition of local symmetries in gray value images by harmonic functions’. Proc. ICPR, Rome, Italy, November 1988, pp. 345347.
    46. 46)
      • 31. Qian, K., Schott, M., Zheng, W., et al: ‘Context-based approach of separating contactless captured high-resolution overlapped latent fingerprints’, IET Biometrics, 2014, 3, (2), pp. 101112.
    47. 47)
      • 8. Gottschlich, C., Marasco, E., Yang, A.Y., et al: ‘Fingerprint liveness detection based on histograms of invariant gradients’. Proc. IJCB, Clearwater, FL, USA, September 2014, pp. 17.
    48. 48)
      • 19. Tams, B.: ‘Absolute fingerprint pre-alignment in minutiae-based cryptosystems’. Proc. BIOSIG, Darmstadt, Germany, September 2013, pp. 7586.
    49. 49)
      • 25. Araque, J.L., Baena, M., Chalela, B.E., et al: ‘Synthesis of fingerprint images’. Proc. 16th Int. Conf. Pattern Recognition (ICPR), 2002, pp. 422425.
    50. 50)
      • 39. Larkin, K.G.: ‘Uniform estimation of orientation using local and nonlocal 2-D energy operators’, Opt. Express, 2005, 13, (20), pp. 80978121.
    51. 51)
      • 45. Sherlock, B.G., Monro, D.M.: ‘A model for interpreting fingerprint topology’, Pattern Recognit., 1993, 26, (7), pp. 10471055.
    52. 52)
      • 53. Salomon, D.: ‘Data Compression’ (Springer, London, UK, 2007, 4th edn.).
    53. 53)
      • 43. Huckemann, S., Hotz, T., Munk, A.: ‘Global models for the orientation field of fingerprints: an approach based on quadratic differentials’, IEEE Trans. Pattern Anal. Mach. Intell., 2008, 30, (9), pp. 15071517.
    54. 54)
      • 28. Gottschlich, C., Huckemann, S.: ‘Separating the real from the synthetic: minutiae histograms as fingerprints of fingerprints’, IET Biometrics, 2014, 3, (4), pp. 291301.
    55. 55)
      • 48. Cappelli, R., Maltoni, D., Turroni, F.: ‘Benchmarking local orientation extraction in fingerprint recognition’. Proc. 20th Int. Conf. Pattern Recognition (ICPR), Istanbul, Turkey, August 2010, pp. 11441147.
    56. 56)
      • 9. Gottschlich, C.: ‘Convolution comparison pattern: an efficient local image descriptor for fingerprint liveness detection’, PLoS ONE, 2016, 11, (2), p. e0148552.
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