Advertisement

An Introduction to Fingerprint Presentation Attack Detection

  • Javier Galbally
  • Julian FierrezEmail author
  • Raffaele Cappelli
Chapter
Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Abstract

This chapter provides an introduction to Presentation Attack Detection (PAD), also coined anti-spoofing, in fingerprint biometrics, and summarizes key developments for that purpose in the last two decades. After a review of selected literature in the field, we also revisit the potential of quality assessment for presentation attack detection. We believe that, beyond the interest that the described techniques may intrinsically have by themselves, the case study presented may serve as an example of how to develop and validate fingerprint PAD techniques based on common and publicly available benchmarks and following a systematic and replicable protocol.

Notes

Acknowledgements

This work was done in the context of the TABULA RASA and BEAT projects funded under the 7th Framework Programme of EU. This work was supported by project CogniMetrics from MINECO/FEDER under Grant TEC2015-70627-R, and the COST Action CA16101 (Multi-Foresee).

References

  1. 1.
    van der Putte T, Keuning J (2000) Biometrical fingerprint recognition: don’t get your fingers burned. In: Proceedings of the IFIP conference on smart card research and advanced applications, pp 289–303CrossRefGoogle Scholar
  2. 2.
    Matsumoto T, Matsumoto H, Yamada K, Hoshino S (2002) Impact of artificial gummy fingers on fingerprint systems. In: Proceedings of the SPIE optical security and counterfeit deterrence techniques IV, vol 4677, pp 275–289Google Scholar
  3. 3.
    Thalheim L, Krissler J (2002) Body check: biometric access protection devices and their programs put to the test. ct magazine, pp 114–121Google Scholar
  4. 4.
    Sousedik C, Busch C (2014) Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biom 3(14):219–233. http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2013.0020CrossRefGoogle Scholar
  5. 5.
    Derakhshani R, Schuckers S, Hornak L, O’Gorman L (2003) Determination of vitality from non-invasive biomedical measurement for use in fingerprint scanners. Pattern Recognit 36:383–396CrossRefGoogle Scholar
  6. 6.
    Antonelli A, Capelli R, Maio D, Maltoni D (2006) Fake finger detection by skin distortion analysis. IEEE Trans Inf Forensics Secur 1:360–373CrossRefGoogle Scholar
  7. 7.
    Galbally J, Alonso-Fernandez F, Fierrez J, Ortega-Garcia J (2012) A high performance fingerprint liveness detection method based on quality related features. Future Gener Comput Syst 28:311–321CrossRefGoogle Scholar
  8. 8.
    Franco A, Maltoni D (2008) Advances in biometrics: sensors, algorithms and systems, chap. fingerprint synthesis and spoof detection. Springer, Berlin, pp 385–406CrossRefGoogle Scholar
  9. 9.
    Li SZ (ed) (2009) Encyclopedia of biometrics. Springer, BerlinGoogle Scholar
  10. 10.
    Coli P (2008) Vitality detection in personal authentication systems using fingerprints. PhD thesis, Universita di CagliariGoogle Scholar
  11. 11.
    Sandstrom M (2004) Liveness detection in fingerprint recognition systems. Master’s thesis, Linkoping UniversityGoogle Scholar
  12. 12.
    Lane M, Lordan L (2005) Practical techniques for defeating biometric devices. Master’s thesis, Dublin City UniversityGoogle Scholar
  13. 13.
    Blomme J (2003) Evaluation of biometric security systems against artificial fingers. Master’s thesis, Linkoping UniversityGoogle Scholar
  14. 14.
    Lapsley P, Less J, Pare D, Hoffman N (1998) Anti-fraud biometric sensor that accurately detects blood flowGoogle Scholar
  15. 15.
    Setlak DR (1999) Fingerprint sensor having spoof reduction features and related methodsGoogle Scholar
  16. 16.
    Kallo I, Kiss A, Podmaniczky JT (2001) Detector for recognizing the living character of a finger in a fingerprint recognizing apparatusGoogle Scholar
  17. 17.
    Diaz-Santana E, Parziale G (2008) Liveness detection methodGoogle Scholar
  18. 18.
    Kim J, Choi H, Lee W (2011) Spoof detection method for touchless fingerprint acquisition apparatusGoogle Scholar
  19. 19.
    Centro Criptologico Nacional (CCN) (2011) Characterizing attacks to fingerprint verification mechanisms CAFVM v3.0. Common Criteria PortalGoogle Scholar
  20. 20.
    Bundesamt fur Sicherheit in der Informationstechnik (BSI) (2008) Fingerprint spoof detection protection profile FSDPP v1.8. Common Criteria PortalGoogle Scholar
  21. 21.
    Marcialis GL, Lewicke A, Tan B, Coli P, Grimberg D, Congiu A, Tidu A, Roli F, Schuckers S (2009) First international fingerprint liveness detection competition – livdet 2009. In: Proceedings of the IAPR international conference on image analysis and processing (ICIAP). LNCS, vol 5716, pp 12–23CrossRefGoogle Scholar
  22. 22.
    Ghiani L, Yambay DA, Mura V, Marcialis GL, Roli F, Schuckers SA (2017) Review of the fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015. Image Vis Comput 58:110–128CrossRefGoogle Scholar
  23. 23.
    Galbally J, Fierrez J, Alonso-Fernandez F, Martinez-Diaz M (2011) Evaluation of direct attacks to fingerprint verification systems. J Telecommun Syst Special Issue Biom Syst Appl 47:243–254CrossRefGoogle Scholar
  24. 24.
    Abhyankar A, Schuckers S (2009) Integrating a wavelet based perspiration liveness check with fingerprint recognition. Pattern Recognit 42:452–464CrossRefGoogle Scholar
  25. 25.
    Biometrics Institute: Biometric Vulnerability Assessment Expert Group (2011). http://www.biometricsinstitute.org/pages/biometric-vulnerability-assessment-expert-group-bvaeg.html
  26. 26.
    NPL: National Physical Laboratory: Biometrics (2010). http://www.npl.co.uk/biometrics
  27. 27.
    CESG: Communications-Electronics Security Group - Biometric Working Group (BWG) (2001). https://www.cesg.gov.uk/policyguidance/biometrics/Pages/index.aspx
  28. 28.
    BEAT: Biometrics Evaluation and Testing (2016). http://www.beat-eu.org/
  29. 29.
    TABULA RASA: Trusted biometrics under spoofing attacks (2014). http://www.tabularasa-euproject.org/
  30. 30.
    Maltoni D, Maio D, Jain A, Prabhakar S (2009) Handbook of fingerprint recognition. Springer, BerlinCrossRefGoogle Scholar
  31. 31.
    Cappelli R, Maio D, Lumini A, Maltoni D (2007) Fingerprint image reconstruction from standard templates. IEEE Trans Pattern Anal Mach Intell 29:1489–1503CrossRefGoogle Scholar
  32. 32.
    Cappelli R (2009) Handbook of fingerprint recognition, chapter, synthetic fingerprint generation. Springer, Berlin, pp 270–302Google Scholar
  33. 33.
    Hadid A, Evans N, Marcel S, Fierrez J (2015) Biometrics systems under spoofing attack: an evaluation methodology and lessons learned. IEEE Signal Process Mag 32(5):20–30CrossRefGoogle Scholar
  34. 34.
    Galbally J, Marcel S, Fierrez J (2014) Image quality assessment for fake biometric detection: application to iris, fingerprint and face recognition. IEEE Trans on Image Process 23(2):710–724MathSciNetCrossRefGoogle Scholar
  35. 35.
    Sousedik C, Busch C (2014) Presentation attack detection methods for fingerprint recognition systems: a survey. IET Biometrics 3(4):219–233CrossRefGoogle Scholar
  36. 36.
    Marasco E, Ross A (2015) A survey on anti-spoofing schemes for fingerprint recognition systems. ACM Comput Surv 47(2):1–36CrossRefGoogle Scholar
  37. 37.
    Pinto A, Pedrini H, Krumdick M, Becker B, Czajka A, Bowyer KW, Rocha A (2018) Counteracting presentation attacks in face, fingerprint, and iris recognition. In: Vatsa M, Singh R, Majumdar A (eds) Deep learning in biometrics. CRC PressGoogle Scholar
  38. 38.
    Wehde A, Beffel JN (1924) Fingerprints can be forged. Tremonia Publish Co, ChicagoGoogle Scholar
  39. 39.
    de Water MV (1936) Can fingerprints be forged? Sci News-Lett 29:90–92CrossRefGoogle Scholar
  40. 40.
    Sengottuvelan P, Wahi A (2007) Analysis of living and dead finger impressions identification for biometric applications. In: Proceedings of the international conference on computational intelligence and multimedia applicationsGoogle Scholar
  41. 41.
    Yoon S, Feng J, Jain AK (2012) Altered fingerprints: analysis and detection. IEEE Trans Pattern Anal Mach Intell 34:451–464Google Scholar
  42. 42.
    Willis D, Lee M (1998) Biometrics under our thumb. Netw Comput http://www.networkcomputing.com/
  43. 43.
    Sten A, Kaseva A, Virtanen T (2003) Fooling fingerprint scanners - biometric vulnerabilities of the precise biometrics 100 SC scanner. In: Proceedings of the australian information warfare and IT security conferenceGoogle Scholar
  44. 44.
    Wiehe A, Sondrol T, Olsen K, Skarderud F (2004) Attacking fingerprint sensors. Technical report NISlab, Gjovik University CollegeGoogle Scholar
  45. 45.
    Galbally J, Cappelli R, Lumini A, de Rivera GG, Maltoni D, Fierrez J, Ortega-Garcia J, Maio D (2010) An evaluation of direct and indirect attacks using fake fingers generated from ISO templates. Pattern Recognit Lett 31:725–732Google Scholar
  46. 46.
    Barral C, Tria A (2009) Fake fingers in fingerprint recognition: glycerin supersedes gelatin. In: Formal to Practical Security. LNCS, vol 5458, pp 57–69CrossRefGoogle Scholar
  47. 47.
    Parthasaradhi S, Derakhshani R, Hornak L, Schuckers S (2005) Time-series detection of perspiration as a liveness test in fingerprint devices. IEEE Trans Syst Man Cybern - Part C: Appl Rev 35:335–343CrossRefGoogle Scholar
  48. 48.
    Schuckers S, Abhyankar A (2004) A wavelet based approach to detecting liveness in fingerprint scanners. In: Proceeding of the biometric authentication workshop (BioAW). LNCS, vol 5404. Springer, Berlin, pp 278–386Google Scholar
  49. 49.
    Tan B, Schuckers S (2006) Comparison of ridge- and intensity-based perspiration liveness detection methods in fingerprint scanners. In: Proceeding of the SPIE biometric technology for human identification III (BTHI III), vol 6202, p 62020AGoogle Scholar
  50. 50.
    Tan B, Schuckers S (2009) A new approach for liveness detection in fingerprint scanners based on valley noise analysis. J Electron Imaging 17:011,009CrossRefGoogle Scholar
  51. 51.
    DeCann B, Tan B, Schuckers S (2009) A novel region based liveness detection approach for fingerprint scanners. In: Proceeding of the IAPR/IEEE international conference on biometrics. LNCS, vol 5558. Springer, Berlin, pp 627–636CrossRefGoogle Scholar
  52. 52.
    NexIDBiometrics: (2012). http://nexidbiometrics.com/
  53. 53.
    Abhyankar A, Schuckers S (2006) Fingerprint liveness detection using local ridge frequencies and multiresolution texture analysis techniques. In: Proceedings of the IEEE international conference on image processing (ICIP)Google Scholar
  54. 54.
    Marasco E, Sansone C (2010) An anti-spoofing technique using multiple textural features in fingerprint scanners. In: Proceeding of the IEEE workshop on biometric measurements and systems for security and medical applications (BIOMS), pp 8–14Google Scholar
  55. 55.
    Marasco E, Sansone C (2012) Combining perspiration- and morphology-based static features for fingerprint liveness detection. Pattern Recognit Lett 33:1148–1156CrossRefGoogle Scholar
  56. 56.
    Cappelli R, Maio D, Maltoni D (2001) Modelling plastic distortion in fingerprint images. In: Proceedings of the international conference on advances in pattern recognition (ICAPR). LNCS, vol 2013. Springer, Berlin, pp 369–376zbMATHGoogle Scholar
  57. 57.
    Bazen AM, Gerez SH (2003) Fingerprint matching by thin-plate spline modelling of elastic deformations. Pattern Recognit 36:1859–1867CrossRefGoogle Scholar
  58. 58.
    Chen Y, Dass S, Ross A, Jain AK (2005) Fingerprint deformation models using minutiae locations and orientations. In: Proceeding of the IEEE workshop on applications of computer vision (WACV), pp 150–156Google Scholar
  59. 59.
    Chen Y, Jain AK (2005) Fingerprint deformation for spoof detection. In: Proceeding of the IEEE biometric symposium (BSym), pp 19–21Google Scholar
  60. 60.
    Zhang Y, Tian J, Chen X, Yang X, Shi P (2007) Fake finger detection based on thin-plate spline distortion model. In: Proceeding of the IAPR international conference on biometrics. LNCS, vol 4642. Springer, Berlin, pp 742–749Google Scholar
  61. 61.
    Yau WY, Tran HT, Teoh EK, Wang JG (2007) Fake finger detection by finger color change analysis. In: Proceedings of the international conference on biometrics (ICB). LNCS, vol 4642. Springer, Berlin, pp 888–896Google Scholar
  62. 62.
    Jia J, Cai L (2007) Fake finger detection based on time-series fingerprint image analysis. In: Proceedings of the IEEE international conference on intelligent computing (ICIC). LNCS, vol 4681. Springer, Berlin, pp 1140–1150Google Scholar
  63. 63.
    Marcialis GL, Roli F, Tidu A (2010) Analysis of fingerprint pores for vitality detection. In: Proceedings of the IEEE international conference on pattern recognition (ICPR), pp 1289–1292Google Scholar
  64. 64.
    Memon S, Manivannan N, Balachandran W (2011) Active pore detection for liveness in fingerprint identification system. In: Proceeding of the IEEE Telecommuncations Forum (TelFor), pp 619–622Google Scholar
  65. 65.
    Martinsen OG, Clausen S, Nysather JB, Grimmes S (2007) Utilizing characteristic electrical properties of the epidermal skin layers to detect fake fingers in biometric fingerprint systems-a pilot study. IEEE Trans Biomed Eng 54:891–894CrossRefGoogle Scholar
  66. 66.
    Moon YS, Chen JS, Chan KC, So K, Woo KC (2005) Wavelet based fingerprint liveness detection. Electron Lett 41CrossRefGoogle Scholar
  67. 67.
    Nikam SB, Agarwal S (2009) Feature fusion using gabor filters and cooccrrence probabilities for fingerprint antispoofing. Int J Intell Syst Technol Appl 7:296–315CrossRefGoogle Scholar
  68. 68.
    Nikam SB, Argawal S (2009) Ridgelet-based fake fingerprint detection. Neurocomputing 72:2491–2506CrossRefGoogle Scholar
  69. 69.
    Nikam S, Argawal S (2010) Curvelet-based fingerprint anti-spoofing. Signal Image Video Process 4:75–87CrossRefGoogle Scholar
  70. 70.
    Coli P, Marcialis GL, Roli F (2007) Power spectrum-based fingerprint vitality detection. In: Proceedings of the IEEE workshop on automatic identification advanced technologies (AutoID), pp 169–173Google Scholar
  71. 71.
    Jin C, Kim, H, Elliott S (2007) Liveness detection of fingerprint based on band-selective Fourier spectrum. In: Proceedings of the international conference on information security and cryptology (ICISC). LNCS, vol 4817. Springer, Berlin, pp 168–179Google Scholar
  72. 72.
    Jin S, Bae Y, Maeng H, Lee H (2010) Fake fingerprint detection based on image analysis. In: Proceedings of the SPIE, Sensors, cameras, and systems for industrial/scientific applications XI, vol 7536, p 75360CGoogle Scholar
  73. 73.
    Lee H, Maeng H, Bae Y (2009) Fake finger detection using the fractional Fourier transform. In: Proceedings of the biometric ID management and multimodal communication (BioID). LNCS, vol 5707. Springer, Berlin, pp 318–324CrossRefGoogle Scholar
  74. 74.
    Marcialis GL, Coli P, Roli F (2012) Fingerprint liveness detection based on fake finger characteristics. Int J Digit Crime Forensics 4CrossRefGoogle Scholar
  75. 75.
    Coli P, Marcialis GL, Roli F (2007) Vitality detection from fingerprint images: a critical survey. In: Proceedings of the international conference on biometrics (ICB). LNCS, vol 4642. Springer, Berlin, pp 722–731Google Scholar
  76. 76.
    Coli P, Marcialis GL, Roli F (2008) Fingerprint silicon replicas: static and dynamic features for vitality detection using an optical capture device. Int J Image Graph, pp 495–512CrossRefGoogle Scholar
  77. 77.
    Marcialis GL, Coli P, Roli F (2012) Fingerprint liveness detection based on fake finger characteristics. Int J Digit Crime Forensics 4:1–19CrossRefGoogle Scholar
  78. 78.
    Choi H, Kang R, Choi K, Jin ATB, Kim J (2009) Fake-fingerprint detection using multiple static features. Optic Eng 48:047, 202CrossRefGoogle Scholar
  79. 79.
    Nixon KA, Rowe RK (2005) Multispectral fingerprint imaging for spoof detection. In: Proceedings of the SPIE, biometric technology for human identification II (BTHI), vol 5779, pp 214–225Google Scholar
  80. 80.
    Rowe RK, Nixon KA, Butler PW (2008) Advances in biometrics: Sensors, algorithms and systems, Chapter, multispectral fingerprint image acquisition. Springer, Berlin, pp 3–23CrossRefGoogle Scholar
  81. 81.
    Yau WY, Tran HL, Teoh EK (2008) Fake finger detection using an electrotactile display system. In: Proceedings of the international conference on control, automation, robotics and vision (ICARCV), pp 17–20Google Scholar
  82. 82.
    Reddy PV, Kumar A, Rahman SM, Mundra TS (2008) A new antispoofing approach for biometric devices. IEEE Trans Biomed Circuits Syst 2:328–337CrossRefGoogle Scholar
  83. 83.
    Baldiserra D, Franco A, Maio D, Maltoni D (2006) Fake fingerprint detection by odor analysis. In: Proceedings of the IAPR international conference on biometrics (ICB). LNCS, vol 3832. Springer, Berlin, pp 265–272Google Scholar
  84. 84.
    Cheng Y, Larin KV (2006) Artificial fingerprint recognition using optical coherence tomography with autocorrelation analysis. Appl Opt 45:9238–9245CrossRefGoogle Scholar
  85. 85.
    Manapuram RK, Ghosn M, Larin KV (2006) Identification of artificial fingerprints using optical coherence tomography technique. Asian J Phys 15:15–27Google Scholar
  86. 86.
    Cheng Y, Larin KV (2007) In vivo two- and three-dimensional imaging of artificial and real fingerprints with optical coherence tomography. IEEE Photonics Technol Lett 19:1634–1636CrossRefGoogle Scholar
  87. 87.
    Larin KV, Cheng Y (2008) Three-dimensional imaging of artificial fingerprint by optical coherence tomography. In: Proceedings of the SPIE biometric technology for human identification (BTHI), vol 6944, p 69440MGoogle Scholar
  88. 88.
    Chang S, Larin KV, Mao Y, Almuhtadi W, Flueraru C (2011) State of the art in biometrics, chap. fingerprint spoof detection using near infrared optical analysis, Intechopen, pp 57–84Google Scholar
  89. 89.
    Nasiri-Avanaki MR, Meadway A, Bradu A, Khoshki RM, Hojjatoleslami A, Podoleanu AG (2011) Anti-spoof reliable biometry of fingerprints using en-face optical coherence tomography. Opt Photonics J 1:91–96CrossRefGoogle Scholar
  90. 90.
    Rattani A, Poh N, Ross A (2012) Analysis of user-specific score characteristics for spoof biometric attacks. In: Proceedings of the IEEE computer society workshop on biometrics at the international conference on computer vision and pattern recognition (CVPR), pp 124–129Google Scholar
  91. 91.
    Marasco E, Ding Y, Ross A (2012) Combining match scores with liveness values in a fingerprint verification system. In: Proceedings of the IEEE international conference on biometrics: theory, applications and systems (BTAS), pp 418–425Google Scholar
  92. 92.
    Hariri M, Shokouhi SB (2011) Possibility of spoof attack against robustness of multibiometric authentication systems. SPIE J Opt Eng 50:079, 001CrossRefGoogle Scholar
  93. 93.
    Akhtar Z, Fumera G, Marcialis GL, Roli F (2011) Robustness analysis of likelihood ratio score fusion rule for multi-modal biometric systems under spoof attacks. In: Proceedings of the IEEE international carnahan conference on security technology (ICSST), pp 237–244Google Scholar
  94. 94.
    Akhtar Z, Fumera G, Marcialis GL, Roli F (2012) Evaluation of serial and parallel multibiometric systems under spoofing attacks. In: Proceedings of the international conference on biometrics: theory, applications and systems (BTAS)Google Scholar
  95. 95.
    Ultra-Scan: (2012). http://www.ultra-scan.com/
  96. 96.
    Optel: (2012). http://www.optel.pl/
  97. 97.
  98. 98.
    VirdiTech: (2012). http://www.virditech.com/
  99. 99.
    Kang H, Lee B, Kim H, Shin D, Kim J (2003) A study on performance evaluation of the liveness detection for various fingerprint sensor modules. In: Proceedings of the international conference on knowledge-based intelligent information and engineering systems (KES). LNAI, vol 2774. Springer, Berlin, pp 1245–1253Google Scholar
  100. 100.
    Wang L, El-Maksoud RA, Sasian JM, William Kuhn P, Gee K, 2009, V.S.V (2009) A novel contactless aliveness-testing fingerprint sensor. In: Proceedings of the SPIE novel optical systems design and optimization XII, vol 7429, pp 742–915Google Scholar
  101. 101.
    Bayram S, Avcibas I, Sankur B, Memon N (2006) Image manipulation detection. J Electron Imaging 15:041,102CrossRefGoogle Scholar
  102. 102.
    Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5:492–496CrossRefGoogle Scholar
  103. 103.
    Avcibas I, Memon N, Sankur B (2003) Steganalysis using image quality metrics. IEEE Trans Image Process 12:221–229MathSciNetCrossRefGoogle Scholar
  104. 104.
    Avcibas I, Kharrazi M, Memon N, Sankur B (2005) Image steganalysis with binary similarity measures. EURASIP J Appl Signal Process 1:2749–2757zbMATHGoogle Scholar
  105. 105.
    Lyu S, Farid H (2006) Steganalysis using higher-order image statistics. IEEE Trans Inf Forensics Secur 1:111–119CrossRefGoogle Scholar
  106. 106.
    Lim E, Jiang X, Yau W (2002) Fingerprint quality and validity analysis. In: Proceeding of the IEEE international conference on image processing (ICIP), vol 1, pp 469–472Google Scholar
  107. 107.
    Chen Y, Dass S, Jain A (2005) Fingerprint quality indices for predicting authentication performance. In: Proceedings of the IAPR audio- and video-based biometric person authentication (AVBPA). LNCS, vol 3546. Springer, Berlin, pp 160–170Google Scholar
  108. 108.
    Chen T, Jiang X, Yau W (2004) Fingerprint image quality analysis. In: Proceeding of the IEEE international conference on image processing (ICIP), vol 2, pp 1253–1256Google Scholar
  109. 109.
    Hong L, Wan Y, Jain AK (1998) Fingerprint image enhancement: algorithm and performance evaluation. IEEE Trans Pattern Anal Mach Intell 20(8):777–789Google Scholar
  110. 110.
    Alonso-Fernandez F, Fierrez J, Ortega-Garcia J, Gonzalez-Rodriguez J, Fronthaler H, Kollreider K, Bigun, J (2008) A comparative study of fingerprint image quality estimation methods. IEEE Trans Inf Forensics Secur 2(4):734–743CrossRefGoogle Scholar
  111. 111.
    Bigun J (2006) Vision with direction. Springer, BerlinGoogle Scholar
  112. 112.
    Shen L, Kot A, Koo W (2001) Quality measures of fingerprint images. In: Proceedings of the IAPR audio- and video-based biometric person authentication (AVBPA). LNCS, vol 2091. Springer, Berlin, pp 266–271Google Scholar
  113. 113.
    Wong PW, Pappas TN, Safranek RJ, Chen J, Wang Z, Bovik AC, Simoncelli EP, Sheikh HR (2005) Handbook of image and video processing, Chapter, Sect. VIII: image and video rendering and assessment. Academic Press, New York, pp 925–989Google Scholar
  114. 114.
    Sheikh HRS, Sabir MF, Bovik AC (2006) A statistical evaluation of recent full reference image quality assessment algorithms. IEEE Trans Image Process 15:3440–3451CrossRefGoogle Scholar
  115. 115.
    Saad MA, Bovik AC, Charrier C (2012) Blind image quality assessment: a natural scene statatistics approach in the DCT domain. IEEE Trans Image Process 21:3339–3352MathSciNetCrossRefGoogle Scholar
  116. 116.
    Wang Z, Bovik AC (2009) Mean squared error: love it or leave it? IEEE Signal Process Mag 26:98–117CrossRefGoogle Scholar
  117. 117.
    Teo PC, Heeger DJ (1994) Perceptual image distortion. In: Proceedings of the international conference on image processing, pp 982–986Google Scholar
  118. 118.
    Avcibas I, Sankur B, Sayood K (2002) Statistical evaluation of image quality measures. J Electron Imaging 11:206–223CrossRefGoogle Scholar
  119. 119.
    Huynh-Thu Q, Ghanbari M (2008) Scope of validity of PSNR in image/video quality assessment. Electron Lett 44:800–801CrossRefGoogle Scholar
  120. 120.
    Yao S, Lin W, Ong E, Lu Z (2005) Contrast signal-to-noise ratio for image quality assessment. In: Proceedings of the international conference on image processing (ICIP), pp 397–400Google Scholar
  121. 121.
    Eskicioglu AM, Fisher PS (1995) Image quality measures and their performance. IEEE Trans Commun 43:2959–2965CrossRefGoogle Scholar
  122. 122.
    Martini MG, Hewage CT, Villarini B (2012) Image quality assessment based on edge preservation. Signal Process Image Commun 27:875–882CrossRefGoogle Scholar
  123. 123.
    Nill NB, Bouzas B (1992) Objective image quality measure derived from digital image power spectra. Opt Eng 31:813–825CrossRefGoogle Scholar
  124. 124.
    Liu A, Lin W, Narwaria M (2012) Image quality assessment based on gradient similarity. IEEE Trans Image Process 21:1500–1511MathSciNetCrossRefGoogle Scholar
  125. 125.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612CrossRefGoogle Scholar
  126. 126.
  127. 127.
    Sheikh HR, Bovik AC (2006) Image information and visual quality. IEEE Trans Image Process 15:430–444CrossRefGoogle Scholar
  128. 128.
    Soundararajan R, Bovik AC (2012) RRED indices: reduced reference entropic differencing for image quality assessment. IEEE Trans Image Process 21:517–526MathSciNetCrossRefGoogle Scholar
  129. 129.
    Wang Z, Sheikh HR, Bovik AC (2002) No-reference perceptual quality assessment of JPEG compressed images. In: Proceedings of the IEEE international conference on image processing (ICIP), pp 477–480Google Scholar
  130. 130.
    Zhu X, Milanfar P (2009) A no-reference sharpness metric sensitive to blur and noise. In: Proceedings of the international workshop on quality of multimedia experience (QoMEx), pp 64–69Google Scholar
  131. 131.
    Moorthy AK, Bovik AC (2010) A two-step framework for constructing blind image quality indices. IEEE Signal Process Lett 17:513–516CrossRefGoogle Scholar
  132. 132.
    Mittal A, Soundararajan R, Bovik AC (2012) Making a completely blind image quality analyzer. IEEE Signal Process Lett. https://doi.org/10.1109/LSP.2012.2227726CrossRefGoogle Scholar
  133. 133.
    Harris C, Stephens M (1988) A combined corner and edge detector. In: Proceeding of the alvey vision conference (AVC), pp 147–151Google Scholar
  134. 134.
    Brunet D, Vrscay ER, Wang Z (2012) On the mathematical properties of the structural similarity index. IEEE Trans Image Process 21:1488–1499MathSciNetCrossRefGoogle Scholar
  135. 135.
    Nixon KA, Aimale V, Rowe RK (2008) Handbook of biometrics, Chapter, Spoof detection schemes. Springer, Berlin, pp 403–423Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Javier Galbally
    • 1
  • Julian Fierrez
    • 2
    Email author
  • Raffaele Cappelli
    • 3
  1. 1.European CommissionJoint Research CentreIspraItaly
  2. 2.Universidad Autonoma de MadridMadridSpain
  3. 3.Università di BolognaCesenaItaly

Personalised recommendations