Effectiveness of Camouflage Make-Up Patterns Against Face Detection Algorithms

  • Vojtěch FričEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9730)


The goal of this research was to evaluate which make-up patterns are effective in disrupting face detection algorithms. Three free or open source implementations of various face detection algorithms were selected. These were at first tested on an unaltered dataset. The dataset was then augmented with different make-up patterns. The patterns were chosen arbitrarily with the goal to disrupt the detection algorithms. The results show that the selected patterns decrease the accuracy of the face detection algorithms by about 10 %.


Face detection Make-up camouflage Object detection Computer vision 



This research work has been partly supported by the project SGS-2013-029 of the Czech Ministry of Education, Youth and Sports.


  1. 1.
    Harvey, A.: CV Dazzle: Camouage from face detection (2010).
  2. 2.
    Li, J., Zhang, Y.: Learning SURF cascade for fast and accurate object detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3468–3475, June 2013Google Scholar
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, pp. 511–518 (2001)Google Scholar
  4. 4.
    Köstinger, M., et al.: Annotated facial landmarks in the wild: a largescale, real-world database for facial landmark localization. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops), pp. 2144–2151. IEEE (2011)Google Scholar
  5. 5.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the 2002 International Conference on Image Processing, vol. 1, pp. I–900. IEEE (2002)Google Scholar
  6. 6.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)Google Scholar
  7. 7.
    Huang, G.B., et al.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. Technical report 07-49. University of Massachusetts, Amherst, October 2007Google Scholar
  8. 8.
    Kazemi, V., Sullivan, J.: One millisecond face alignment with an ensemble of regression trees. In: Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1867–1874 (2014)Google Scholar
  9. 9.
    Beier, T., Neely, S.: Feature-based Image Metamorphosis. SIGGRAPH Comput. Graph. 26(2), 35–42 (1992). ISSN: 0097–8930CrossRefGoogle Scholar
  10. 10.
    Jain, V., Learned-Miller, E.: FDDB: A Benchmark for Face Detection in Unconstrained Settings. Technical report UM-CS-2010-009. University of Massachusetts, Amherst (2010)Google Scholar
  11. 11.
    Berg, T.L., et al.: Who’s in the picture. Adv. Neural Inf. Process. Syst. 17, 137–144 (2005)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science and Engineering, Faculty of Applied SciencesUniversity of West BohemiaPlzeňCzech Republic

Personalised recommendations