Affine real-time face tracking using a wavelet network

We present a method for visual face tracking that is based on a wavelet representation of a face template. The wavelet representation allows arbitrary affine variations of the facial image, it allows to generalize from an individual face template to a rather general face template and it allows to adapt the computational needs of the tracking algorithm to the computational resources available. The method presented runs in real-time (25 Hz) on a Linux Pentium 450 MHz and was tested on several common sequences including the salesman-sequence.

[1]  Harold H. Szu,et al.  Neural network adaptive wavelets for signal representation and classification , 1992 .

[2]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Gregory D. Hager,et al.  Efficient Region Tracking With Parametric Models of Geometry and Illumination , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Christoph von der Malsburg,et al.  Tracking and learning graphs and pose on image sequences of faces , 1996, Proceedings of the Second International Conference on Automatic Face and Gesture Recognition.

[5]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[6]  J. P. Jones,et al.  An evaluation of the two-dimensional Gabor filter model of simple receptive fields in cat striate cortex. , 1987, Journal of neurophysiology.

[7]  Christoph von der Malsburg,et al.  Tracking and Learning Graphs on Image Sequences of Faces , 1996, ICANN.

[8]  Shaogang Gong,et al.  View-Based Adaptive Affine Tracking , 1998, ECCV.

[9]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[10]  Shaogang Gong,et al.  Tracking and segmenting people in varying lighting conditions using colour , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[11]  S. McKenna,et al.  Recognising Moving Faces , 1998 .

[12]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Norbert Krüger,et al.  Face Recognition by Elastic Bunch Graph Matching , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Ingrid Daubechies,et al.  The wavelet transform, time-frequency localization and signal analysis , 1990, IEEE Trans. Inf. Theory.

[15]  J. E. Glynn,et al.  Numerical Recipes: The Art of Scientific Computing , 1989 .

[16]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Qinghua Zhang,et al.  Wavelet networks , 1992, IEEE Trans. Neural Networks.

[18]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.