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Latent overlapped fingerprint separation: a review

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Abstract

Fingerprint matching is a widely used process to aid in crime scene investigation, where fingerprint fragments are often found on objects and surfaces. In such cases, the lifted fingerprints (called latents) are usually of poor quality and often appear overlapped and against a noisy background. These aspects make latent fingerprint – especially overlapped latent fingerprints – segmentation and enhancement (for subsequent matching) a difficult problem, for which several solutions have been proposed during the past few years. This paper presents an overview of contemporary techniques for overlapped fingerprint separation in the context of latent overlapped fingerprint matching. In addition to explaining the main concepts and surveying the literature in the field, it highlights the importance of the overlapped fingerprint segmentation (ROI extraction) process, a step for which there are no automatic techniques yet.

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Notes

  1. Manual segmentation is still the norm in the current state of the art. In Section 3.5.2 we present preliminary results for an algorithm capable of performing automatic ROI extraction of component fingerprint regions – background (B), single fingerprint (S), and overlapped fingerprints (O) – from the overlapped fingerprint image.

  2. Rank-1 identification accuracy (ideal case: rank-1 = 100 %) is the most important accuracy measure. Rank-n identification accuracy is the ratio of query templates for which correct (genuine) comparisons are among the first n comparisons (i.e., the n comparisons with highest similarity value in database). Rank-1 identification accuracy shows how many genuine comparisons were assigned the highest similarity value [33].

References

  1. Ashbaugh D (1999) Quantitative-qualitative friction ridge analysis: an introduction to basic and advanced ridgeology. CRC Press

  2. Bazen AM, Gerez SH (2002) Systematic methods for the computation of the directional fields and singular points of fingerprints. IEEE Trans Pattern Anal Mach Intell 24(7):905–919

    Article  Google Scholar 

  3. Bigun J (1987) Optimal orientation detection of linear symmetry

  4. Chen F, Feng J, Jain A, Zhou J, Zhang J (2011) Separating overlapped fingerprints. IEEE Trans Inf Forensics Secur 6(2):346–359. ISSN 1556-6013. doi:10.1109/TIFS.2011.2114345

    Article  Google Scholar 

  5. Chikkerur S, Cartwright AN, Govindaraju V (2007) Fingerprint enhancement using stft analysis. Pattern Recogn 40(1):198–211

    Article  MATH  Google Scholar 

  6. Choi H, Boaventura M, Boaventura IA, Jain A (2012) Automatic segmentation of latent fingerprints. In: IEEE 5th international conference on biometrics: theory, applications and systems (BTAS), 2012. IEEE, pp 303–310

  7. Dass SC (2004) Markov random field models for directional field and singularity extraction in fingerprint images. IEEE Trans Image Process 13(10):1358–1367

    Article  Google Scholar 

  8. Daugman JG (1985) Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. JOSA A 2 (7):1160–1169

    Article  Google Scholar 

  9. DeCann B, Ross A (2013) Relating ROC and CMC curves via the biometric menagerie. In: IEEE 6th international conference on biometrics: theory, applications and systems (BTAS), 2013, pp 1–8. doi:10.1109/BTAS.2013.6712705

  10. Dror IE, Charlton D (2006) Why experts make errors. J Forensic Identif 56 (4):600

    Google Scholar 

  11. Dror IE, Peron AE, Hind S-L, Charlton D (2005) When emotions get the better of us: the effect of contextual top-down processing on matching fingerprints. Appl Cogn Psychol 19(6):799–809

    Article  Google Scholar 

  12. Dror IE, Charlton D, Péron AE (2006) Contextual information renders experts vulnerable to making erroneous identifications. Forensic Sci Int 156(1):74–78

    Article  Google Scholar 

  13. Dror IE, Champod C, Langenburg G, Charlton D, Hunt H, Rosenthal R (2011) Cognitive issues in fingerprint analysis: inter-and intra-expert consistency and the effect of a target comparison. Forensic Sci Int 208(1):10–17

    Article  Google Scholar 

  14. Feng J, Shi Y, Zhou J (2012) Robust and efficient algorithms for separating latent overlapped fingerprints. IEEE Trans Inf Forensics Secur 7(5):1498–1510. ISSN 1556-6013. doi:10.1109/TIFS.2012.2204254

    Article  Google Scholar 

  15. Feng J, Zhou J, Jain A (2013) Orientation field estimation for latent fingerprint enhancement. IEEE Trans Pattern Anal Mach Intell 35(4):925–940. ISSN 0162-8828. doi:10.1109/TPAMI.2012.155

    Article  Google Scholar 

  16. Fraser-Mackenzie PA, Dror IE, Wertheim K (2013) Cognitive and contextual influences in determination of latent fingerprint suitability for identification judgments. Sci Justice 53(2):144– 153

    Article  Google Scholar 

  17. Garris MD, McCabe RM (2000) Fingerprint minutiae from latent and matching tenprint images. In: Tenprint Images, National Institute of Standards and Technology. Citeseer

  18. Gu J, Zhou J, Yang C (2006) Fingerprint recognition by combining global structure and local cues. IEEE Trans Image Process 15(7):1952–1964

    Article  Google Scholar 

  19. Hall LJ, Player E (2008) Will the introduction of an emotional context affect fingerprint analysis and decision-making? Forensic Sci Int 181(1):36–39

    Article  Google Scholar 

  20. Hong L, Wan Y, Jain A (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789

    Article  Google Scholar 

  21. Huckemann S, Hotz T, Munk A (2008) Global models for the orientation field of fingerprints: an approach based on quadratic differentials. IEEE Trans Pattern Anal Mach Intell 30(9):1507–1519

    Article  Google Scholar 

  22. Hyvärinen A., Karhunen J, Oja E (2004) Independent component analysis, vol 46. Wiley

  23. Jain A, Feng J (2009) Latent palmprint matching. IEEE Trans Pattern Anal Mach Intell 31(6):1032–1047. ISSN 0162-8828. doi:10.1109/TPAMI.2008.242

    Article  Google Scholar 

  24. Kamei T (2004) Image filter design for fingerprint enhancement. In: Automatic fingerprint recognition systems. Springer, pp 113–126

  25. Karimi-Ashtiani S, Kuo C-CJ (2008) A robust technique for latent fingerprint image segmentation and enhancement. In: 15th IEEE international conference on image processing, 2008. ICIP 2008. IEEE, pp 1492–1495

  26. Kass M, Witkin A (1987) Analyzing oriented patterns. Comput Vis Graph Image Process 37(3):362– 385

    Article  Google Scholar 

  27. Langenberg G (2009) Precision, reproducibility, repeatability, and biasability of conclusions resulting from the ace-v process. J Forensic Identif 59(2):219

    Google Scholar 

  28. Liu M, Jiang X, Kot AC (2005) Fingerprint reference-point detection. EURASIP J Adv Signal Process 2005(4):1–12

    Article  MATH  Google Scholar 

  29. Lo PZ, Luo Y (2006) Methods and apparatus for adaptive hierarchical processing of print images. US Patent App. 11/456,622

  30. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain A (2002) FVC2002: 2nd fingerprint verification competition. In: Proceedings of 16th international conference on pattern recognition, 2002, vol 3. IEEE, pp 811–814

  31. Maio D, Maltoni D, Cappelli R, Wayman JL, Jain AK (2002) FVC2002: the 2nd international fingerprint verification competition. http://bias.csr.unibo.it/fvc2002/

  32. Maltoni D, Maio D, Jain A, Prabhakar S (2009) Handbook of fingerprint recognition. Springer Science & Business Media

  33. I. Management Association. Machine Learning: Concepts, Methodologies, Tools and Applications: Concepts, Methodologies, Tools and Applications. Premier reference source. Information Science Reference. ISBN 9781609608194. https://books.google.rs/books?id=1GWcHmCrl0QC (2011)

  34. Marques ACPB, Thome ACG (2005) A neural network fingerprint segmentation method. IEEE, pp 6–pp

  35. Neurotechnology. VeriFinger SDK: Fingerprint identification for stand-alone or Web solutions. http://www.neurotechnology.com/verifinger.html (2016)

  36. O’Gorman L, Nickerson JV (1989) An approach to fingerprint filter design. Pattern Recogn 22(1):29–38

    Article  Google Scholar 

  37. Oliveira M, Leite NJ (2008) A multiscale directional operator and morphological tools for reconnecting broken ridges in fingerprint images. Pattern Recogn 41(1):367–377

    Article  MATH  Google Scholar 

  38. Pelillo M, Refice M (1994) Learning compatibility coefficients for relaxation labeling processes. IEEE Trans Pattern Anal Mach Intell 16(9):933–945

    Article  Google Scholar 

  39. Pelillo M, Abbattista F, Maffione A (1993) Evolutionary learning for relaxation labeling processes. In: Advances in artificial intelligence. Springer, pp 230–241

  40. Prabhakar S et al (2008) Probabilistic orientation field estimation for fingerprint enhancement and verification. In: Biometrics symposium, 2008. BSYM’08. IEEE, pp 41–46

  41. Ratha NK, Chen S, Jain AK (1995) Adaptive flow orientation-based feature extraction in fingerprint images. Pattern Recogn 28(11):1657–1672

    Article  Google Scholar 

  42. Rosenfeld A, Hummel RA, Zucker SW (1976) Scene labeling by relaxation operations. IEEE Trans Syst Man Cybern 6:420–433

    Article  MathSciNet  MATH  Google Scholar 

  43. Sankaran A, Dhamecha TI, Vatsa M, Singh R (2011) On matching latent to latent fingerprints. In: International joint conference on biometrics (IJCB), 2011. IEEE, pp 1–6

  44. Sankaran A, Vatsa M, Singh R (2012) Hierarchical fusion for matching simultaneous latent fingerprint. In: IEEE 5th international conference on biometrics: theory, applications and systems (BTAS), 2012. IEEE, pp 377–382

  45. Sankaran A, Vatsa M, Singh R (2014) Latent fingerprint matching: a survey. IEEE Access 2:982–1004. ISSN 2169-3536. doi:10.1109/ACCESS.2014.2349879

    Article  Google Scholar 

  46. Sherlock BG, Monro DM (1993) A model for interpreting fingerprint topology. Pattern Recogn 26(7):1047–1055

    Article  Google Scholar 

  47. Shi Y, Feng J, Zhou J (2011) Separating overlapped fingerprints using constrained relaxation labeling. In: Proceedings of international joint conference on biometrics

  48. Short NJ, Hsiao MS, Abbott AL, Fox EA (2011) Latent fingerprint segmentation using ridge template correlation. In: 4th international conference on imaging for crime detection and prevention 2011 (ICDP 2011). IET, pp 1–6

  49. Singh M, Singh DK, Kalra PK (2008) Fingerprint separation: an application of ica, vol 6982, pp 69820L–69820L–11. doi:10.1117/12.777541

  50. Standard for the documentation of analysis, comparison, evaluation, and verification (ace-v) (latent). http://www.swgfast.org/documents/documentation/121124_Standard-Documentation-ACE-V_2.0.pdf (2012)

  51. Stojanović B, Nešković A, Marques O (2016) A novel neural network based approach to latent overlapped fingerprints separation. Multimedia Tools Appl 1-25:1573–7721. doi:10.1007/s11042-016-3696-4

  52. Stojanovic B, Neskovic A, Popovic Z, Lukic V (2014) ANN based fingerprint image ROI segmentation. In: 22nd telecommunications forum telfor (TELFOR), 2014. IEEE, pp 505–508

  53. Stojanović B., Nešković A., Marques O (2015) Fingerprint ROI segmentation using fourier coefficients and neural networks. IEEE, pp 484–487

  54. Van Tilborg HC, Jajodia S (2014) Encyclopedia of cryptography and security. Springer Science & Business Media

  55. Wang Y, Hu J, Phillips D (2007) A fingerprint orientation model based on 2d fourier expansion (fomfe) and its application to singular-point detection and fingerprint indexing. IEEE Trans Pattern Anal Mach Intell 29(4):573–585

    Article  Google Scholar 

  56. Wang YA, Hu J (2011) Global ridge orientation modeling for partial fingerprint identification. IEEE Trans Pattern Anal Mach Intell 33(1):72–87

    Article  Google Scholar 

  57. Wertheim K, Langenburg G, Moenssens A (2006) A report of latent print examiner accuracy during comparison training exercises. J Forensic Identif 56(1):55

    Google Scholar 

  58. Yadav R, Kalra PK, John J (2006) Neural network learning with generalized-mean based neuron model. Soft Comput 10(3):257–263

    Article  Google Scholar 

  59. Yoon S, Feng J, Jain A (2010) On latent fingerprint enhancement. In: SPIE defense, security, and sensing. International Society for Optics and Photonics, pp 766707–766707

  60. Yoon S, Feng J, Jain A (2011) Latent fingerprint enhancement via robust orientation field estimation. In: International joint conference on biometrics (IJCB), 2011, pp 1–8. doi:10.1109/IJCB.2011.6117482

  61. Zhang J, Lai R, Kuo C-CJ (2012) Latent fingerprint segmentation with adaptive total variation model. In: 5th IAPR international conference on biometrics (ICB), 2012. IEEE, pp 189–195

  62. Zhang J, Lai R, Kuo C-J (2012) Latent fingerprint detection and segmentation with a directional total variation model. In: 19th IEEE international conference on image processing (ICIP), 2012. IEEE, pp 1145–1148

  63. Zhang N, Yang X, Zang Y, Jia X, Tian J (2014) Overlapped fingerprints separation based on adaptive orientation model fitting. In: 22nd international conference on pattern recognition (ICPR), 2014, pp 678–683. doi:10.1109/ICPR.2014.127

  64. Zhang N, Zang Y, Yang X, Jia X, Tian J (2014) Adaptive orientation model fitting for latent overlapped fingerprints separation. IEEE Trans Inf Forensics Secur 9(10):1547–1556. ISSN 1556-6013. doi:10.1109/TIFS.2014.2340573

    Article  Google Scholar 

  65. Zhao Q, Jain A (2012) Model based separation of overlapping latent fingerprints. IEEE Trans Inf Forensics Secur 7 (3):904–918. ISSN 1556-6013. doi:10.1109/TIFS.2012.2187281

    Article  Google Scholar 

  66. Zhou J, Gu J (2004) A model-based method for the computation of fingerprints’ orientation field. IEEE Trans Image Process 13(6):821–835

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Electrical Engineering, University of Belgrade, 73 Kralja Aleksandra Blvd, 11120, Belgrade, Serbia

    Branka Stojanović & Aleksandar Nešković

  2. Vlatacom Institute Ltd Belgrade, 5 Milutina Milankovica Blvd, 11070, Belgrade, Serbia

    Branka Stojanović

  3. Florida Atlantic University, Boca Raton, FL, 33431, USA

    Oge Marques

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  1. Branka Stojanović
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Correspondence to Oge Marques.

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Stojanović, B., Marques, O. & Nešković, A. Latent overlapped fingerprint separation: a review. Multimed Tools Appl 76, 16263–16290 (2017). https://doi.org/10.1007/s11042-016-3908-y

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