A Virtual Environment Tool for Benchmarking Face Analysis Systems

  • Mauricio Correa
  • Javier Ruiz-del-Solar
  • Rodrigo Verschae
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


In this article, a virtual environment for realistic testing of face analysis systems under uncontrolled conditions is proposed. The key elements of this tool are a simulator, and real face and background images taken under real-world conditions with different acquisition conditions, such as indoor or outdoor illumination. Inside the virtual environment, an observing agent, the one with the ability to recognize and detect faces, can navigate and observe the face images, at different distances, and angles. During the face analysis process, the agent can actively change its viewpoint and relative distance to the faces in order to improve the recognition results. The virtual environment provides all behaviors to the agent (navigation, positioning, face’s image composing under different angles, etc.), except the ones related with the analysis of faces (detection, recognition, pose estimation, etc.). In addition we describe different kinds of experiments that can be implemented for quantifying the face analysis capabilities of agents and provide usage example of the proposed tool in evaluating a face recognition system in a service robot.


Face analysis Face Recognition Face Recognition Benchmark Evaluation Methodologies Virtual Simulation Environment Simulator 


  1. 1.
    Abate, A.F., Nappi, M., Riccio, D., Sabatino, G.: 2D and 3D face recognition: A survey. Pattern Recognition Letters 28, 1885–1906 (2007)CrossRefGoogle Scholar
  2. 2.
    Face Recognition Home Page, (available on June 4, 2012)
  3. 3.
    BeFIT - Benchmarking Facial Image Analysis Technologies home page, (avalilable on July 5, 2012)
  4. 4.
    Gao, W., Cao, B., Shan, S., Chen, X., Zhou, D., Zhang, X., Zhao, D.: The CAS-PEAL Large-Scale Chinese Face Database and Baseline Evaluations. Trans. Sys. Man Cyber. Part A 38(1), 149–161 (2008)CrossRefGoogle Scholar
  5. 5.
    Labeled Faces in the Wild Database, (available on June 5, 2012)
  6. 6.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments. University of Massachusetts, Amherst, Technical Report 07-49 (October 2007)Google Scholar
  7. 7.
    Ruiz-del-Solar, J., Verschae, R., Correa, M.: Recognition of Faces in Unconstrained Environments: A Comparative Study. EURASIP Journal on Advances in Signal Processing 2009, Article ID 184617, 19 pages (2009)Google Scholar
  8. 8.
    Face Recognition Grand Challenge, Official website public site, (available on June 30, 2010)
  9. 9.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing J. 16(5), 295–306 (1998)CrossRefGoogle Scholar
  10. 10.
    Phillips, P.J., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Hoffman, K., Marques, J., Min, J., Worek, W.: Overview of the Face Recognition Grand Challenge. In: Proc. of the IEEE Conf. Computer Vision and Pattern Recognition – CVPR 2005, vol. 1, pp. 947–954 (2005)Google Scholar
  11. 11.
    Gross, R.: Face Databases. In: Li, S., Jain, A.K. (eds.) Handbook of Face Recognition, pp. 301–327. Springer (2005)Google Scholar
  12. 12.
    Yale University Face Image Database public site, (available on June 5, 2012)
  13. 13.
    BioID Face Database public site, (available on June 5, 2012)
  14. 14.
    AR Face Database public site, (available on June 30, 2010)
  15. 15.
    Flynn, P.J., Bowyer, K.W., Phillips, P.J.: Assessment of time dependency in face recognition: An initial study. In: Audio and Video-Based Biometric Person Authentication, pp. 44–51 (2003)Google Scholar
  16. 16.
    Yale Face Database B. Public site, (available on June 30, 2010)
  17. 17.
    PIE Database. Basic information, (available on June 30, 2010)
  18. 18.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. on Patt. Analysis and Machine Intell. 28(12), 2037–2041 (2006)CrossRefGoogle Scholar
  19. 19.
    Viola, P., Jones, M.: Robust Real-Time Face Detection. Int. J. Comput. Vision 57(2), 137–154 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mauricio Correa
    • 1
    • 2
  • Javier Ruiz-del-Solar
    • 1
    • 2
  • Rodrigo Verschae
    • 2
  1. 1.Department of Electrical EngineeringUniversidad de ChileChile
  2. 2.Advanced Mining Technology CenterUniversidad de ChileChile

Personalised recommendations