Fingerprint algorithms
Algorithmes de reconnaissance d'empreintes digitales

Comparison methods / méthodes de comparaison

If manual comparison by a fingerprint expert is always done to say if two fingerprint images are coming from the same finger in critical cases, automated methods are widely used now.

Si un expert en empreintes digitales effectue toujours une comparaison manuelle pour dire si deux images d'empreintes proviennent du même doigt dans les cas critiques, les méthodes automatiques sont à présent partout utilisées.


Many different algorithm types exist: / De nombreuses techniques existent:


Fingerprint categories general direction

fingerprint minutiae
  • Most algorithms are using minutiae, the specific points like ridges ending, bifurcation... Only the position and direction of these features are stored in the signature for further comparison.
    La plupart des algorithmes utilisent les minuties, ces points spécifiques comme les fin de lignes, les embranchements... Seules les positions et directions de ces points sont stockées dans la signature de l'empreinte pour les futures comparaisons.
  • Minutiae definition
    Ridge count
  • Some algorithms counts the number of ridges between particular points, generally the minutiae, instead of the distances computed from the position.
    Certains algorithmes comptent le nombre de lignes entre deux points, généralement des minuties, au lieu des distances calculées à partir des positions.

  • Pattern matching algorithms are using the general shape of the ridges. The fingerprint is divided in small sectors, and the ridge direction, phase and pitch are extracted and stored.
    Les algorithmes basés sur la corrélation utilisent la forme générale des lignes. L'empreinte est divisée en petits secteurs où la direction, la phase et le pas des lignes sont extraits puis stockés.

  • Very often, algorithms are using a combination of all theses techniques.
    Il est fréquent que les algorithmes combinent ces diverses techniques.

    Matching of a "ten print card" fingerprint with a latent fingerprint using minutiae.
    Accord entre une image d'empreinte provenant d'un enregistrement sur fiche (fiche de police où les 10 doigts encrées sont imprimés) et une empreinte latente, utilisant les minuties.

    ten print card matching latent print


    What is the probability of two fingerprints to match?
    Quelle est la probabilité que deux empreintes soient identiques?

    "On the individuality of fingerprints" (Pankanti/IBM) estimated to 6.10-8 the probability for 12 minutiae matching among 36 to match, but lots of estimations exist...
    L'article "On the individuality of fingerprints" (Pankanti/IBM) estime à 6.10-8 la probabilité que 12 minuties parmi 36 correspondent, mais de nombreuses estimations existent...

    In the following array, M & R defines "regions", N minutiae.
    Dans le tabeau suivant, M & R sont les "régions", et N le nombre de minuties.

    matching probabilities

    Sensor + algorithm integration

    Integrating the recognition algorithm with the sensor has started in 1999.


    Very small sensors

    The previous algorithms just cannot work with very small sensors, because there is not enough minutiae information. As a result, some companies are developping new algorithms -well, not that new because it is based on simple pattern matching (what else?!?).

    They are announcing sometimes very good numbers, but you just cannot get the source information to get these numbers and have to trust them.

    Here are the main announcements:

    Hey, guys, even with a FAR = 1/10 000, what you are saying is that with two independant finger areas as small as 4x4mm, you can recognize someone with a FAR = 1/100 000 000, much more better than all those governmental companies working on that topic for decades? Maybe the actual sensors are giving much more better images (something hard to believe with 300 microns thickness of glass above the sensor), and that you are not comparing your images with latent prints, but well, be humble (and prove your numbers...)

    Within this fierce competition, a company is going the other way, demonstrating that we need large sensors:

    But well, for sure, if you are using minutia-based algorithms, then large sensors are a must. I demonstrated that years ago with Cogent:

    which does not mean that algorithms using 3rd level features (for instance pores) do not work with a smaller area.


    Evaluation

    FAR & FRR evaluation is difficult, and you must show the origin of the numbers so that we can understand and compare. It is very easy to trick the databases, this is pretty well explained in this paper:

  • How to evaluate a fingerprint algorithm / Patrik Lindeberg | Precise Biometrics / 2015-06-25
  • But in fact, this is a little bit more complicated, because you also have to explain the enrollment strategy, you can also read my accuracy page to get some more info to make your mind.


    Police & fingerprints
    La police et les empreintes digitales

    The FBI IAFIS homepage

    The FBI latent print homepage


    Accuracy and reliability of forensic latent fingerprint decisions

  • Accuracy and reliability of forensic latent fingerprint decisions / Bradford T. Ulery, R. Austin Hicklin, JoAnn Buscagli and Maria Antonia Roberts (local copy for the records)
  • This paper reports on the first large-scale study of the accuracy and reliability of latent print examiners’ decisions, in which 169 latent print examiners each compared approximately 100 pairs of latent and exemplar fingerprints from a pool of 744 pairs.

    So it will tell you how accurate is a "human" algorithm...


    Fingerprint algorithms:

    See also the fingerprint sensors as some sensor manufacturers are including the authentication software in their offer.
    Voir aussi les capteurs d'empreintes car certains fabriquants de capteurs incluent leur soft d'authentification dans leur offre.

    Take care: the last update is dated around 2006.


    1. 123 ID (USA) [CVT]
    2. Acter (Switzerland) [acterBIOLIB]
    3. ActivCard (Canada)
    4. Aldebaran Systems (USA) [participated to FVC2002]
    5. Antheus Technology (Brasil, USA) [Agora]
    6. APRO Technology (Japan)
    7. AST (Spain) [ASTAS]
    8. Astro Datensysteme (Germany) [BioTouch]
    9. Av@lon Systems / Semantic system (Switzerland) [Ultramatch] [participated to FpEVT 2003, NIST]
    10. Beijing BaiXinTong Electronic Tech (China) [participated to FVC2006]
    11. Beijing Smackbio Technology (China) [Smackfinger] [participated to FVC2006]
    12. Beijing HanWang Technology (China) [participated to FVC2004]
    13. Bergdata (CDVI) (Germany, France) [bdfis]
    14. BeyondLSI (Japan)
    15. BioCert (USA?) [BioCert]
    16. BIO-Key (USA) [VST]
    17. Biolink (USA) [U-Match]
    18. Biometrix (Austria) [BioCheck]
    19. Bionopoly (USA)
    20. Bioscrypt (Canada, USA) [Bioscrypt Core, V-Pass...]
    21. Bromba (Germany) (spin-off from Siemens, end 2003)
    22. CASIA Institute of Automation, Chinese Academy of Sciences (China) [participated to FVC2004]
    23. Casio (Japan) [VeriPat]
    24. CBA-Japan (Japan) [FCHIP2]
    25. The Chinese University of Hong-Kong (Hong-Kong) [for smartcard (2004)]
    26. Cogent (USA) [Bioswipe] is 3M (2010)
    27. ComnetiX (Canada)
    28. Cottonwood (CCS) (seems no more available 03/2004)
    29. Count Me In (USA) [LightningID] (Digital Persona?)
    30. CrossMatch (USA) [ID 500...]
    31. Daimin (Korea)
    32. Dalian University of Technology (China) [FVC2006]
    33. Datamicro (Russia) [participated to FVC2002, FVC2004]
    34. DDS Digital Development Systems (Japan) [UBF]
    35. Dermalog (Germany)
    36. Beijing Fingerpass / labs: Digital Fingerpass Corporation (China)
    37. DTK Digital Tech Korea
    38. Ekey (Austria) [TOCAxxx]
    39. Fidelica (USA) 27 feb 2004: announced the availability of the FBA-4001 matching algorithm.
    40. Fingerpin AG (Switzerland) (May 2004: access problem to website)
    41. FIST (Korea) [Finguard]
    42. Fujitsu (Japan)
    43. Futronic Technology (China) [participated to FVC2004]
    44. Genologic (Germany)
    45. Gevarius (Russia) [participated to FVC2004]
    46. Golden Finger Systems (USA?) [participated to FpEVT 2003, NIST]
    47. Griaule (Brasil) [Pequi]
    48. GuangZhou Comet Technology Development (China) [participated to FVC2006]
    49. Hyundai (Korea) [FDI(fingerPrint Digital Identity)]
    50. HZMS Biometrics (China) [participated to FVC2002]
    51. ID Solutions (USA & Russia)
    52. ID3 semiconductors (France) [Biothentic, Certis]
    53. Idencom (Germany) [Biokey]
    54. Ident (Germany)
    55. Identalink (Germany) [BioPassport]
    56. Identix (USA) (acquired Identicator 1999, merge with Visionics 2002)
    57. Beijing IDWorld (China)
    58. iFingerSys (Austria) linked to Joanneum Research
    59. Ikendi (Germany) [IKxxx]
    60. Init(Brasil) [FBIC]
    61. ImageWare Systems (USA) [IWS biometric engine]
    62. Innovatrics (France/Slovakia) [Iengine] created by Jan Lunter (France) [participated to FVC2004]
    63. Integral Ltd (Russia) [participated to FVC2004] link to TestTech
    64. ISL (UK) [SentryNET]
    65. Jaypeetex (India)
    66. JM Tronics (Malaysia)
    67. Labcal (Canada) [.smartprint]
    68. LGE Institute of Technology (Korea) [FVC2006]
    69. Mantra Technologies (India) [FVS engine]
    70. Miaxis (China) [eBioxxxx]
    71. Morphosoric (Germany)
    72. Motorola (USA) (acquired Printrak 2000).
    73. NEC Solutions America (USA)
    74. Neurodynamics (UK) [Deixis]
    75. Neurotechnology (Lithuania) (formerly Neurotechnologija / 2008) [VeriFinger]
    76. Nexign (Korea)
    77. NitGen (Korea)
    78. NIST (USA)
    79. Nyoun (Korea) [participated to FVC2004]
    80. ODI (USA)
    81. Optel (Poland)
    82. Papillon Systems (Russia)
    83. PrintScan / Computer Experts [WinFing]
    84. Qingsong (China)
    85. Raytheon (USA)
    86. Sagem Morpho (France)
    87. SecuIt (Korea) [Secuxxx]
    88. Sense Holdings (USA) [BioCode]
    89. Shanghai Tongji / Smartech (USA, China)
    90. Shanghai Fingertech Information (China)
    91. Siemens (Austria)
    92. Silex (Japan)
    93. Sonda (Russia)
    94. Startek (Taiwan)
    95. Suprema (Korea) [Unifinger]
    96. Technoimagia (Japan) [FP-xxx]
    97. TeKey Research Group (Israel)
    98. Thales Security Systems (France)
    99. The Phoenix Group (USA) [Afix tracker]
    100. Testech inc. (Korea) [Bio-I]
    101. Shanghai Tongji Smartech (China) [Corn Technology]
    102. Ultrascan (USA)
    103. Unicomp technology (China) [FVC2006]
    104. Veridt (formerly Biocentric Solutions, May 2004) (USA) [BioHub, BioSentry]
    105. Warwick Warp (UK) [WarpFinger]
    106. Xi'an Qingsong Tech Co (China) [Timeasy, Touchsmart]
    107. Zaklad Techniki Mikroprocesorowej (Poland) [participated to FVC2004]
    108. Zefyr (France)
    109. ZK software (China) [ZK finger]

    110. Independent developers:
      Développeurs indépendants:

    111. Andrei Nikiforov (now at Bio-key?)
    112. Ariel Unanue (Argentina) [BiofingerAI] [participated to FVC2004]
    113. Christian Pötzsch (Germany 2001) Fingerprint Detection System Auf der Basis von Neuronalen Netzen
    114. Deng Guoquiang (China) [participated to FVC2004, FVC2006]
    115. Ilya Belogin (Russia) [participated to FVC2006]
    116. Ilya Poshivaylo (Russia) [participated to FVC2006]
    117. Ji Hui (China) [participated to FVC2004, FVC2006]
    118. Li Lijuan (China) [participated to FVC2004]
    119. Song Yong (China) [participated to FVC2006]
    120. Wei Wang (China) [participated to FVC2006]
    121. Xu Zengbo (China) [participated to FVC2006]
    122. Yang Qianbang (China) [participated to FVC2006]
    123. Yasar TUTUK & Semsi Cihan Yucel (Turquie) [Inspector]