A system identification approach for video-based face recognition

The paper poses video-to-video face recognition as a dynamical system identification and classification problem. We model a moving face as a linear dynamical system whose appearance changes with pose. An autoregressive and moving average (ARMA) model is used to represent such a system. The choice of ARMA model is based on its ability to take care of the change in appearance while modeling the dynamics of pose, expression etc. Recognition is performed using the concept of sub space angles to compute distances between probe and gallery video sequences. The results obtained are very promising given the extent of pose, expression and illumination variation in the video data used for experiments.

[1]  Mukhtar M. Ali.  Analysis of stationary spatial-temporal processes: Estimation and prediction , 1979 .

[2]  Josef Kittler,et al.  Proceedings of the 4th International Conference on Pattern Recognition , 1988 .

[3]  C. Tomasi.  Detection and Tracking of Point Features , 1991 .

[4]  Bart De Moor,et al.  Subspace algorithms for the stochastic identification problem, , 1993, Autom..

[5]  H H Bülthoff,et al.  How are three-dimensional objects represented in the brain? , 1994, Cerebral cortex.

[6]  Ken-ichi Maeda,et al.  Face recognition using temporal image sequence , 1998, Proceedings Third IEEE International Conference on Automatic Face and Gesture Recognition.

[7]  A. Weinstein.  Almost invariant submanifolds for compact group actions , 1999, math/9908133.

[8]  B. Moor,et al.  Subspace angles and distances between ARMA models , 2000 .

[9]  Richard J. Martin.  A metric for ARMA processes , 2000, IEEE Trans. Signal Process..

[10]  R Chellappa,et al.  Face verification through tracking facial features. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[11]  Stefano Soatto,et al.  Recognition of human gaits , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[12]  Zoran Biuk,et al.  Face recognition from multi-pose image sequence , 2001, ISPA 2001. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis. In conjunction with 23rd International Conference on Information Technology Interfaces (IEEE Cat..

[13]  Trevor Darrell,et al.  Face Recognition from Long-Term Observations , 2002, ECCV.

[14]  Rama Chellappa,et al.  Probabilistic recognition of human faces from video , 2002, Proceedings. International Conference on Image Processing.

[15]  Stefano Soatto,et al.  Editable dynamic textures , 2002, SIGGRAPH '02.

[16]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[17]  David J. Kriegman,et al.  Video-based face recognition using probabilistic appearance manifolds , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[18]  Lior Wolf,et al.  Kernel principal angles for classification machines with applications to image sequence interpretation , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[19]  Tsuhan Chen,et al.  Video-based face recognition using adaptive hidden Markov models , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[20]  Stefano Soatto,et al.  Dynamic Textures , 2003, International Journal of Computer Vision.