On the Impact of Alterations on Face Photo Recognition Accuracy

  • Matteo Ferrara
  • Annalisa Franco
  • Davide Maltoni
  • Yunlian Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8156)


This work is framed into the context of automatic face recognition in electronic identity documents. In particular we study the impact of digital alteration of the face images used for enrollment on the recognition accuracy. Alterations can be produced both unintentionally (e.g., by the acquisition or printing device) or intentionally (e.g., people modify images to appear more attractive). Our results show that state-of-the-art algorithms are sufficiently robust to deal with some alterations whereas other kinds of degradation can significantly affect the accuracy, thus requiring the adoption of proper detection mechanisms.


ICAO eMRTD face recognition image alteration digital beautification 


  1. 1.
    Bourlai, T., Ross, A., Jain, A.K.: On matching digital face images against passport photos. In: 2009 International Conference on Biometrics, Identity and Security (BIdS), pp. 1–10 (2009)Google Scholar
  2. 2.
    Bourlai, T., Ross, A., Jain, A.K.: Restoring degraded face images for matching faxed or scanned photos. IEEE Transactions on Information Forensics and Security 6(2), 371–384 (2011)CrossRefGoogle Scholar
  3. 3.
    Biometric Deployment of Machine Readable Travel Documents. ICAO (2004)Google Scholar
  4. 4.
    ISO/IEC 19794-5, Information technology - Biometric data interchange formats - Part 5: Face image data (2011)Google Scholar
  5. 5.
    Vass, G., Perlaki, T.: Applying and removing lens distortion in post production. In: Proceedings of 2nd Hungarian Conference on Computer Graphics and Geometry (2003)Google Scholar
  6. 6.
    LiftMagic - Instant cosmetic surgery and anti-aging makeover tool (2013),
  7. 7.
    Neurotechnology Inc. Neurotechnology web site (2013),
  8. 8.
    Luxand Inc. Luxand Web Site (2013),
  9. 9.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Bicego, M., Grosso, A., Tistarelli, M.: On the Use of SIFT Features for Face Authentication. In: Conference on Computer Vision and Pattern Recognition Workshop, CVPRW 2006, p. 35 (2006)Google Scholar
  11. 11.
    Martinez, A.M., Benavente, R.: The AR face database. Computer Vision Center, CVC Technical Report (1998)Google Scholar
  12. 12.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, New York (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Matteo Ferrara
    • 1
  • Annalisa Franco
    • 1
  • Davide Maltoni
    • 1
  • Yunlian Sun
    • 1
  1. 1.Department of Computer Science and EngineeringUniversity of BolognaCesenaItaly

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