An Incremental Learning Algorithm for Face Recognition

  • O. Déniz
  • M. Castrillón
  • J. Lorenzo
  • M. Hernández
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2359)

Abstract

In face recognition, where high-dimensional representation spaces are generally used, it is very important to take advantage of all the available information. In particular, many labelled facial images will be accumulated while the recognition system is functioning, and due to practical reasons some of them are often discarded. In this paper, we propose an algorithm for using this information. The algorithm has the fundamental characteristic of being incremental. On the other hand, the algorithm makes use of a combination of classification results for the images in the input sequence. Experiments with sequences obtained with a real person detection and tracking system allow us to analyze the performance of the algorithm, as well as its potential improvements.

Keywords

face recognition incremental learning face sequences 

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References

  1. [1]
    Y.,S. Gong, and H. Liddell. Exploiting the dynamics of faces in spatio-temporal context. In Procs. The Sixth International Conference on Control, Automation, Robotics and Vision (ICARCV2000), Singapore, December 2000.Google Scholar
  2. [2]
    O. Yamaguchi, K. Fukui, and K. Maeda. Face recognition using temporal image sequence. In IEEE Int. Conf. on Automatic Face and Gesture Recognition, pages 318–323, Nara, 1998.Google Scholar
  3. [3]
    J. Kittler, J. Matas, K. Jonsson, and M.U. Ramos Sánchez. Combining evidence in personal identity verification systems. Pattern Recognition Letters, 18(9):845–852, 1997.CrossRefGoogle Scholar
  4. [4]
    A. J. Howell and H. Buxton. Towards unconstrained face recognition from image sequences. In Procs. of the Second Int. Conference on Automatic Face and Gesture Recognition, Killington, Vermont, October 1996.Google Scholar
  5. [5]
    H. Wechsler, V. Kakkad, J. Huang, S. Gutta, and V. Chen. Automatic video-based person authentication using the RBF network. In First Int’l Conference on Audio and Video-Based Biometric Person Authentication (AVBPA), Crans-Montana, Switzerland, 1997.Google Scholar
  6. [6]
    T. Choudbury, B. Clarkson, T. Jebara, and A. Pentland. Multimodal person recognition using unconstrained audio and video. Technical Report TR-472, MIT Media Lab, 1998.Google Scholar
  7. [7]
    A. Senior. Recognizing faces in broadcast video. In Int. Workshop on Recognition, Analysis and Tracking of Faces and Gestures in Real-Time Systems, Corfu, Greece, Sept. 1999.Google Scholar
  8. [8]
    S. McKenna and S. Gong. Recognising moving faces. In Procs. of the NATO ASI on Face Recognition: From Theory to Applications, Stirling, UK, 1997.Google Scholar
  9. [9]
    K. Okada and C. von der Malsburg. Automatic video indexing with incremental gallery creation: integration of recognition and knowledge acquisition. In Procs. of the Third International Conference on Knowledge-Based Intelligent Information Engineering Systems, pages 431–434, Adelaide, August 1999.Google Scholar
  10. [10]
    J. Weng, C.H. Evans, and W.S. Hwang. An incremental learning method for face recognition under continuous video stream. In Procs. of the Fourth International Conference on Automatic Face and Gesture Recognition, Grenoble, France, March 2000.Google Scholar
  11. [11]
    R. Sukthankar and R. Stockton. Argus: The digital doorman. IEEE Intelligent Systems and their applications, 16(2):14–19, 2001.CrossRefGoogle Scholar
  12. [12]
    E. Bigun, J. Bigun, B. Duc, and S. Fischer. Expert conciliation for multi modal person authentication systems by bayesian statistics. In J. Bigun, G. Chollet, and G. Borgefors, editors, Audio and Video based Person Authentication-AVBPA97, volume LNCS-1206, pages 291–300. Springer, 1997.CrossRefGoogle Scholar
  13. [13]
    F.M Hernández, J. Cabrera, M. Castrillón, and C. Guerra. DESEO: An active vision system for detection, tracking and recognition. In Procs. of the Second International Conference on Automatic Face and Gesture Recognition, Killington, Vermont, October 1996.Google Scholar
  14. [14]
    M. Castrillon, J. Lorenzo, M. Hernandez, and J. Cabrera. Before characterizing faces. In IX Spanish Symposium on Pattern Recognition and Image Analysis, Castellón, Spain, 2001.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • O. Déniz
    • 1
  • M. Castrillón
    • 1
  • J. Lorenzo
    • 1
  • M. Hernández
    • 1
  1. 1.Instituto Universitario de Sistemas Inteligentesy Aplicaciones Numéricas en Ingeniería (IUSIANI)Universidad de Las Palmas de Gran Canaria Edificio Central del Parque Científico-TecnológicoLas PalmasSpain

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