A Face Recognition Based Multiplayer Mobile Game Application

Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)


In this paper, we present a multiplayer mobile game application that aims at enabling individuals play paintball or laser tag style games using their smartphones. In the application, face detection and recognition technologies are utilised to detect and identify the individuals, respectively. In the game, first, one of the players starts the game and invites the others to join. Once everyone joins the game, they receive a notification for the training stage, at which they need to record another player’s face for a short time. After the completion of the training stage, the players can start shooting each other, that is, direct the smartphone to another user and when the face is visible, press the shoot button on the screen. Both the shooter and the one who is shot are notified by the system after a successful hit. To realise this game in real-time, fast and robust face detection and face recognition algorithms have been employed. The face recognition performance of the system is benchmarked on the face data collected from the game, when it is played with up to ten players. It is found that the system is able to identify the players with a success rate of around or over 90% depending on the number of players in the game.


face recognition multiplayer mobile game DCT LBP SVM 


  1. 1.
    Cooper, J.W.: Java Design Patterns: A Tutorial. Addison-Wesley (2000)Google Scholar
  2. 2.
  3. 3.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proc. of CVPR, pp. 511–518 (2001)Google Scholar
  4. 4.
    OpenCV Android SDK (May 2014),
  5. 5.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  6. 6.
    Ekenel, H.K.: A robust face recognition algorithm for real-world applications, PhD dissertation, University of Karlsruhe, TH (2009)Google Scholar
  7. 7.
    Ekenel, H.K., Stiefelhagen, R.: Analysis of local appearance based face recognition: Effects of feature selection and feature normalization. In: CVPR Biometrics Workshop, New York, USA (2006)Google Scholar
  8. 8.
    Shan, C., Shaogang, G., McOwan, P.W.: Facial Expression Recognition Based on Local Binary Patterns: A Comprehensive Study. Image and Vision Computing 27(6), 803–808 (2009)Google Scholar
  9. 9.
    Choi, S.E., Lee, Y.J., Lee, S.J., Park, K.R., Kim, J.: Age estimation using a hierarchical classifier based on global and local facial features. Pattern Recognition 44(6), 1262–1281 (2011)CrossRefzbMATHGoogle Scholar
  10. 10.
    Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery, 121–167 (1998)Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  1. 1.Faculty of Computer and InformaticsIstanbul Technical UniversityIstanbulTurkey

Personalised recommendations