Advertisement

On the Effects of Image Alterations on Face Recognition Accuracy

  • Matteo FerraraEmail author
  • Annalisa Franco
  • Davide Maltoni
Chapter

Abstract

Face recognition in controlled environments is nowadays considered rather reliable, and if face is acquired in proper conditions, a good accuracy level can be achieved by state-of-the-art systems. However, we show that, even under these desirable conditions, some intentional or unintentional face image alterations can significantly affect the recognition performance. In particular, in scenarios where the user template is created from printed photographs rather than from images acquired live during enrollment (e.g., identity documents ), digital image alterations can severely affect the recognition results. In this chapter, we analyze both the effects of such alterations on face recognition algorithms and the human capabilities to deal with altered images.

Keywords

Face Recognition Face Image Recognition Accuracy Equal Error Rate False Acceptance Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgment

The work leading to these results has received funding from the European Community’s Framework Programme (FP7/2007-2013) under grant agreement n° 284862.

References

  1. 1.
    Sun, Y., Tistarelli, M., Maltoni, D.: Structural Similarity based image quality map for face recognition across plastic surgery. In: Proceedings of the IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8 (2013)Google Scholar
  2. 2.
    Ferrara, M., Franco, A., Maltoni, D., Sun, Y.: On the impact of alterations on face photo recognition accuracy. In: Proceedings of the International Conference on Image Analysis and Processing (ICIAP2013), Naples, pp. 743–751 (2013)Google Scholar
  3. 3.
    Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: Proceedings of the IEEE Int. Joint Conference on Biometrics (IJCB), Clearwater, Florida, pp. 1–7 (2014)Google Scholar
  4. 4.
    LiftMagic—Instant cosmetic surgery and anti-aging makeover tool [Online]. http://makeovr.com/liftmagic (2015, Jan)
  5. 5.
    White, D., Kemp, R.I., Jenkins, R., Matheson, M., Burton, A.M.: Passport officers’ errors in face matching. PLos ONE 9(8) (2014)Google Scholar
  6. 6.
    Clark, A., Bourlai, T.: Methodological insights on passport image enhancement. In: SPIE Newsroom: Defense & Security (2013)Google Scholar
  7. 7.
    Bourlai, T., Ross, A., Jain, A.K.: Restoring degraded face images for matching faxed or scanned photos. IEEE Trans. Inf. Forensics Secur. 6(2), 371–384 (2011)Google Scholar
  8. 8.
    Bourlai, T., Ross, A., Jain, A.K.: On matching digital face images against passport photos. In: IEEE International Conference on Biometrics, Identity and Security (2009)Google Scholar
  9. 9.
    Singh, R., et al.: Plastic surgery: a new dimension to face recognition. IEEE Trans. Inf. Forensics Secur. 5(3), 441–448 (2010)CrossRefGoogle Scholar
  10. 10.
    Lakshmiprabha, N.S., Majumder, S.: Face recognition system invariant to plastic surgery. In: Proceedings of International Conference on Intelligent Systems Design and Applications, pp. 258–263 (2012)Google Scholar
  11. 11.
    Mun, M., Deorankar, A.: Implementation of plastic surgery face recognition using multimodal biometric features. Int. J. Comput. Sci. Inform. Technol. 5(3), 3711–3715 (2014)Google Scholar
  12. 12.
    Aggarwal, G., Biswas, S., Flynn, P.J., Bowyer, K.W.: A sparse representation approach to face matching across plastic surgery. In: Proceedings of IEEE workshop on the Applications of Computer Vision, pp. 113–119 (2012)Google Scholar
  13. 13.
    Bhatt, H.S., Bharadwaj, S., Singh, R., Vtsa, M., Noore, A.: Evolutionary granular approach for recognizing faces altered due to plastic surgery. In: Proceedings of IEEE Conference on Automatic Face and Gesture Recognition, pp. 720–725 (2011)Google Scholar
  14. 14.
    Bhatt, H.S., Bharadwaj, S., Singh, R., Vatsa, M.: Recognizing surgically altered face images using multiobjective evolutionary algorithm. IEEE Trans. Inf. Forensics Secur. 8(1), 89–100 (2013)CrossRefGoogle Scholar
  15. 15.
    Liu, X., Shan, S., Chen, X.: Face recognition after plastic surgery: a comprehensive study. In: Proceedings of Asian Conference on Computer Vision, pp. 565–576 (2012)Google Scholar
  16. 16.
    Chude-Olisah, C.C., Sulong, G., Chude-Okonkwo, U.A.K., Hashim, S.Z.M.: Face recognition via edge-based Gabor feature representation for plastic surgery-altered images. EURASIP J. Adv. Sig. Process. (2014)Google Scholar
  17. 17.
    Ghatol, N.P., Paigude, R., Shirke, A.: Image morphing detection by locating tampered pixels with demosaicing algorithms. Int. J. Comput. Appl. 66(8), 23–26 (2013)Google Scholar
  18. 18.
    Wen, D., Han, H., Jain, A.K.: Face spoof detection with image distortion analysis. IEEE Trans. Inf. Forensics Secur. 10(4), 746–761 (2015)Google Scholar
  19. 19.
    Zhen, H., Lee, G., Lee, S.Y.: Integrating two-dimensional morphing and pose estimation for face recognition. J. Inf. Sci. Eng. 30, 257–272 (2014)Google Scholar
  20. 20.
    Padilha, A., Silva, J., Sebastiao, R.: Improving face recognition by video spatial morphing. In: Delac, K., Grgic, M. (eds.) Face Recognition (2007)Google Scholar
  21. 21.
    Zou, X., Kittler, J., Tena, J.: A morphing system for effective human face recognition. In: Proceedings of International Conference on Visual Information Engineering, pp. 215–220 (2008)Google Scholar
  22. 22.
    Kamgar-Parsi, B., Lawson, W., Kamgar-Parsi, B.: Toward development of a face recognition system for watchlist surveillance. IEEE Trans. Pattern Anal. Mach. Intell. 33(10), 1925–1937 (2011)CrossRefGoogle Scholar
  23. 23.
    Chennamma, H.R., Rangarajan, L., Veerabhadrappa: Face identification from manipulated facial images using SIFT. In: Proceedings of 3rd International Conference on Emerging Trends in Engineering and Technology (ICETET), pp. 192–195 (2010)Google Scholar
  24. 24.
    Slama, C.: Manual of Photogrammetry, 4th edn. American Society of Photogrammetry, Falls Church, VA (1980)Google Scholar
  25. 25.
    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
  26. 26.
    Neurotechnology Inc.: Neurotechnology web site [Online]. http://www.neurotechnology.com/ (2015, Jan)
  27. 27.
    Luxand Inc.: Luxand web site [Online]. http://luxand.com (2015, Jan)
  28. 28.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)Google Scholar
  29. 29.
    Bicego, M., Grosso, A., Tistarelli, M.: On the use of SIFT features for face authentication. In: Proceedings of Conference on Computer Vision and Pattern Recognition Workshop, p. 35 (2006)Google Scholar
  30. 30.
    Martinez, A.M., Benavente, R.: The AR face database. Computer Vision Center, CVC Technical Report (1998)Google Scholar
  31. 31.
    Maltoni, D., Maio, D., Jain, A.K., Prabhakar, S.: Handbook of Fingerprint Recognition, 2nd edn. Springer, New York, NJ, USA (2009)Google Scholar
  32. 32.
    FRONTEX: Research and Development Unit. Best Practice Technical Guidelines for Automated Border Control (ABC) Systems,—v2.0 (2012)Google Scholar
  33. 33.
    IATA: Airport with automated border control systems [Online]. http://www.iata.org/whatwedo/stb/maps/Pages/passenger-facilitation.aspx (2015, Jan)
  34. 34.
    Wikipedia: Morphing [Online]. http://en.wikipedia.org/wiki/Morphing (2015, Jan)
  35. 35.
    GIMP: GNU image manipulation program web site [Online]. http://www.gimp.org/ (2015, Jan)
  36. 36.
    GIMP: GIMP animation package [Online]. http://registry.gimp.org/node/18398 (2015, Jan)
  37. 37.
    ISO/IEC 19794-5, Information technology—biometric data interchange formats—part 5: face image data (2011)Google Scholar
  38. 38.
    FRONTEX: FRONTEX Web Site [Online]. http://frontex.europa.eu/ (2014, July)
  39. 39.
    Dorizzi, B., et al.: Fingerprint and on-line signature verification competitions at ICB 2009. In: Proceedings 3rd IAPR/IEEE International Conference on Biometrics (ICB09), Alghero (2009)Google Scholar
  40. 40.
    BioLab: FVC-onGoing web site [Online]. http://biolab.csr.unibo.it/fvcongoing (2015, Jan)
  41. 41.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.J.: The FERET database and evaluation procedure for face-recognition algorithms. Image Vision Comput. 16(5), 295–306 (1998)Google Scholar
  42. 42.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face-recognition algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 22(10), 1090–1104 (2000)Google Scholar
  43. 43.
    Eyedea Recognition Ltd.: Eyedea Recognition Web Site [Online]. http://www.eyedea.cz/ (2015, March)

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Matteo Ferrara
    • 1
    Email author
  • Annalisa Franco
    • 1
  • Davide Maltoni
    • 1
  1. 1.Department of Computer Science and Engineering (DISI)University of BolognaCesenaItaly

Personalised recommendations