Person Identification Using Fast Face Learning of Lifting Dyadic Wavelet Filters

  • Shigeru Takano
  • Koichi Niijima
Conference paper
Part of the Advances in Soft Computing book series (AINSC, volume 30)


A person identification system based on fast face learning of lifting wavelet filters is proposed. The real power of our system lies in fast learning of lifting wavelet filters adaptive to facial parts such as eyes, nose and lips, in a set of training faces. In our system, free parameters in the lifting filter are learned fast by using Newton’s method. The learned parameters are memorized in a database together with the training faces. The lifting filters with the learned parameters in the database are applied to each of video frames which contain faces of a person, and the faces are detected by measuring some kind of distance. A person whose face is detected in a maximum number of frames is identified as a target person. To realize fast face detection, the learned filters are applied only to the skin areas separated from background by using color segmentation. Simulation results show that our person identification algorithm is accurate and fast.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Chai D. and Ngan K.N. (1998), Locating Facial Region of a Head-and-shoulders Color Image, Proceedings of the Third International Conference Automatic Face and Gesture Recognition, pp. 124–129.Google Scholar
  2. 2.
    Takano S., Niijima K. and Abdukirim T. (2003), Fast Face Detection by Lifting Dyadic Wavelet Filters, Proceedings of the IEEE International Conference on Image Processing, pp. 893–896.Google Scholar
  3. 3.
    Takano S., Niijima K. and Kuzume K. (2004), Personal Identification by Multiresolution Analysis of Lifting Dyadic Wavelets, Proceedings of the 12th European Signal Processing Conference, CD-ROM.Google Scholar
  4. 4.
    Yang M.-H., Kriegman D. and Ahuja N. (2002), Detecting Faces in Images: A Survey, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol. 24, no. 1, pp. 34–58.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Shigeru Takano
    • 1
  • Koichi Niijima
    • 1
  1. 1.Kyushu UniversityKasuga, FukuokaJapan

Personalised recommendations