Detecting Morphed Face Images Using Facial Landmarks

  • Ulrich ScherhagEmail author
  • Dhanesh BudhraniEmail author
  • Marta Gomez-BarreroEmail author
  • Christoph BuschEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10884)


With the widespread deployment of automatic biometric recognition systems, some security issues have been unveiled. In particular, face recognition systems have been recently shown to be vulnerable to attacks carried out with morphed face images. Such synthetic images can be defined as the fusion of the face images of two (or more) different subjects. The associated risk lies on the ability of multiple subjects to be positively verified with a single enrolled morphed face image. As common texture based features have limited capabilities to tackle this problem, we propose a novel method for morphed face image detection, based on the computation of the differences between the landmarks of a probe bona fide (i.e., captured under supervision) image of the attacker, and the landmarks of the enrolled image (i.e., the suspected morphed image). In this work, a new database is created for the experiments, comprising both bona fide and morphed images created with two different morphing methods. The experiments show that for the detection task, the proposed algorithm achieves Equal Error Rates at 32.7%.


  1. 1.
    International Organization for Standardization: Information technology - Vocabulary - Part 37: Biometrics. ISO/IEC 2382–37:2012, JTC 1/SC 37, Geneva, Switzerland (2012)Google Scholar
  2. 2.
    International Civil Aviation Organization: Machine readable passports - part 9 - deployment of biometric identification and electronic storage of data in emrtds (2015)Google Scholar
  3. 3.
    Ferrara, M., Franco, A., Maltoni, D.: The magic passport. In: Proc. IEEE Int. Joint Conf. on Biometrics, IEEE (sep 2014)Google Scholar
  4. 4.
    Ferrara, M., Franco, A., Maltoni, D.: On the Effects of Image Alterations on Face Recognition Accuracy. In: Face Recognition Across the Imaging Spectrum. Springer Int. Pub. (2016)CrossRefGoogle Scholar
  5. 5.
    Raghavendra, R., Raja, K.B., Busch, C.: Detecting Morphed Face Images. In: Proc. 8th IEEE Int. Conf. on Biometrics: Theory, Applications, and Systems. (2016)Google Scholar
  6. 6.
    Scherhag, U., Ramachandra, R., Raja, K.B., Gomez-barrero, M., Rathgeb, C., Busch, C.: On the Vulnerability and Detection of Digital Morphed and Scanned Face Images. In: Proc. Int. Workshop on Biometrics and Forensics (IWBF). (2017)Google Scholar
  7. 7.
    Gomez-Barrero, M., Rathgeb, C., Scherhag, U., Busch, C.: Is Your Biometric System Robust to Morphing Attacks? In: Proc. Int. Workshop on Biometrics and Forensics (IWBF), Coventry, IEEE (2017)Google Scholar
  8. 8.
    International Organization for Standardization: Information Technology - Biometric presentation attack detection - Part 3: Testing and reporting. ISO/IEC FDIS 30107–3:2017, JTC 1/SC 37, Geneva, Switzerland (2017)Google Scholar
  9. 9.
    Kannala, J., Rahtu, E.: BSIF: Binarized statistical image features. 21st Int. Conf. on Pattern Recognition (ICPR) (2012)Google Scholar
  10. 10.
    Ferrara, M., Franco, A., Maltoni, D.: Face demorphing. IEEE Transactions on Information Forensics and Security 13(4), 1008–1017 (2018)CrossRefGoogle Scholar
  11. 11.
    Scherhag, U., Rathgeb, C., Busch, C.: Towards detection of morphed face images in electronic travel documents. In: 13th IAPR Workshop on Document Analysis Systems (DAS). (2018) 1–6Google Scholar
  12. 12.
    Hildebrandt, M., Neubert, T., Makrushin, A., Dittmann, J.: Benchmarking face morphing forgery detection: Application of stirtrace for impact simulation of different processing steps. In: Proc. Int. Workshop on Biometrics and Forensics (IWBF). (2017) 1–6Google Scholar
  13. 13.
    Kraetzer, C., Makrushin, A., Neubert, T., Hildebrandt, M., Dittmann, J.: Modeling attacks on photo-ID documents and applying media forensics for the detection of facial morphing. In: Proc. Workshop on Information Hiding and Multimedia Security (IH & MMSec). (2017) 21–32Google Scholar
  14. 14.
    Shi, J., Samal, A., Marx, D.: How effective are landmarks and their geometry for face recognition? Comput. Vis. Image Underst. 102(2) (May 2006)CrossRefGoogle Scholar
  15. 15.
    King, D.E.: Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research 10 (2009)Google Scholar
  16. 16.
    Martinez, A.: The AR face database. CVC Tech. Report, Technical report (1998)Google Scholar
  17. 17.
    International Organization for Standardization: Information technology - Biometric data interchange formats - Part 5: Face image data. ISO/IEC 19794–5:2011, JTC 1/SC 37, Geneva, Switzerland (2011)Google Scholar
  18. 18.
    Scherhag, U., Nautsch, A., Rathgeb, C., Gomez-Barrero, M., Veldhuis, R., Spreeuwers, L., Schils, M., Maltoni, D., Grother, P., Marcel, S., Breithaupt, R., Raghavendra, R., Busch, C.: Biometric systems under morphing attacks: Assessment of morphing techniques and vulnerability reporting. In: Int. Conf. of the Biometrics Special Interest Group (BIOSIG). (2017) 1–12Google Scholar
  19. 19.
    Cognitec: FaceVACS-SDK. Accessed: 2017–04-28
  20. 20.
    Martin, A., et al.: The DET Curve in Assessment of Detection Task Performance. Proc, Eurospeech (1997)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.da/sec – Security Research GroupHochschule DarmstadtDarmstadtGermany
  2. 2.DTU ComputeDanmarks Tekniske UniversitetKongens LyngbyDenmark

Personalised recommendations