Advertisement

Using Face Quality Ratings to Improve Real-Time Face Recognition

  • Karl Axnick
  • Ray Jarvis
  • Kim C. Ng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5414)

Abstract

A Face Quality Rating (FQR) is a value derived from a face image that indicates the probability that the face image will be successfully recognized by a specific face recognition method. The FQR can be used as a pre-filter in real-time environments where thousands of face images can be captured every second by multiple surveillance cameras. With so many captured face images, face recognition methods need to strategically decide which face images to attempt recognition on, as it is prohibitively difficult to attempt recognition on all of the images. The FQR pre-filter optimizes processor time utilization resulting in more people being recognized (faster and more accurately) before they leave the surveillance cameras’ views. We generate FQR values using Multiple Layered Perceptron (MLP) neural networks. We then use these MLPs in a real-time environment to experimentally prove that FQR pre-filtering improves the speed and accuracy of any real-time face recognition method...

Keywords

Face Recognition Recognition Rate Face Image Face Detection Virtual Image 
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.

References

  1. 1.
    Nastar, C., Mitschke, M.: Real-time face recognition using feature combination. In: Third IEEE International Conference on Automatic Face and Gesture Recognition. Proceedings, 14-16 April 1998, pp. 312–317 (1998)Google Scholar
  2. 2.
    Gorodnichy, D.O.: Video-based framework for face recognition in video. In: The 2nd Canadian Conference on Computer and Robot Vision. Proceedings, 9-11 May 2005, pp. 330–338 (2005)Google Scholar
  3. 3.
    Donald, T.: The Pattern Recognition Basis of Artificial Intelligence, 388 pages. Wiley-IEEE Computer Society Press (1998)Google Scholar
  4. 4.
    Yongsheng, G., Leung, M.K.H.: Face recognition using line edge map. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(6), 764–779 (2002)CrossRefGoogle Scholar
  5. 5.
    Ming, Z., Fulcher, J.: Face recognition using artificial neural network group-based adaptive tolerance (GAT) trees. IEEE Transactions on Neural Networks 7(3), 555–567 (1996)CrossRefGoogle Scholar
  6. 6.
    Quan, Y., Thangali, A., Sclaroff, S.: Face Identification by a Cascade of Rejection Classifiers. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 20-26 June, vol. 3, p. 152 (2005)Google Scholar
  7. 7.
    Shan, D., Ward, R.: Wavelet-based illumination normalization for face recognition. In: IEEE International Conference on Image Processing, ICIP 2005, 11-14 September, vol. 2, pp. II 954–II 957 (2005)Google Scholar
  8. 8.
    Quanren, X., Jaynes, C.: Mugshot database acquisition in video surveillance networks using incremental auto-clustering quality measures. In: IEEE Conference on Advanced Video and Signal Based Surveillance. Proceedings, 21-22 July 2003, pp. 191–198 (2003)Google Scholar
  9. 9.
    Subasic, M., Loncaric, S., Petkovic, T., Bogunovic, H., Krivec, V.: Face image validation system: Image and Signal Processing and Analysis. In: Proceedings of the 4th International Symposium on Image and Signal Processing and Analysis, ISPA 2005, 15-17 September, pp. 30–33 (2005)Google Scholar
  10. 10.
    Bennett Jr., H.H., Campbell Jr., R.L., Younan, N.H.: Use of rejection class to enhance airborne imagery classification. In: Conference Record of the Thirty-Third Asilomar Conference on Signals, Systems, and Computers, 1999, 24-27 October 1999, vol. 2, pp. 845–848 (1999)Google Scholar
  11. 11.
    Ming-Hsuan, Y., Kriegman, D.J., Ahuja, N.: Detecting faces in images: a survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002)CrossRefGoogle Scholar
  12. 12.
    Lech, M., Hua, Y.: Vector quantization of images using neural networks and simulated annealing. In: Proceedings of the 1991 IEEE Workshop on Neural Networks for Signal Processing [1991], 30 September-1 October 1991, pp. 552–561 (1991)Google Scholar
  13. 13.
    Sim, T., Baker, S., Bsat, M.: The CMU Pose, Illumination, and Expression Database. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(12), 1615–1618 (2003)CrossRefGoogle Scholar
  14. 14.
    Axnick, K., Jarvis, R.: Face and Pose Recognition for Robotic Surveillance. In: Proceedings of the 2005 Australasian Conference on Robotics and Automation (2005)Google Scholar
  15. 15.
    Axnick, K., Ng, K.: Fast Face Recognition: Image and Vision Computing Conference On, New Zealand University of Otago, Dunedin, 28-29 November (2005)Google Scholar
  16. 16.
    Anbang, X., Xin, J., Yugang, J., Ping, G.: Complete Two-Dimensional PCA for Face Recognition. In: 18th International Conference on Pattern Recognition, ICPR 2006, vol. 3, pp. 481–484 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Karl Axnick
    • 1
  • Ray Jarvis
    • 1
  • Kim C. Ng
    • 1
  1. 1.Monash UniversityClaytonAustralia

Personalised recommendations