Probabilistic Human Recognition from Video

This paper presents a method for incorporating temporal information in a video sequence for the task of human recognition. A time series state space model, parameterized by a tracking state vector and a recognizing identity variable, is proposed to simultaneously characterize the kinematics and identity. Two sequential importance sampling (SIS) methods, a brute-force version and an efficient version, are developed to provide numerical solutions to the model. The joint distribution of both state vector and identity variable is estimated at each time instant and then propagated to the next time instant. Marginalization over the state vector yields a robust estimate of the posterior distribution of the identity variable. Due to the propagation of identity and kinematics, a degeneracy in posterior probability of the identity variable is achieved to give improved recognition. This evolving behavior is characterized using changes in entropy. The effectiveness of this approach is illustrated using experimental results on low resolution face data and upper body data.

[1]  Simon J. Godsill,et al.  On sequential Monte Carlo sampling methods for Bayesian filtering , 2000, Stat. Comput..

[2]  Timothy F. Cootes,et al.  Improving identification performance by integrating evidence from sequences , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Anil K. Jain,et al.  Statistical Pattern Recognition: A Review , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Rama Chellappa,et al.  Simultaneous tracking and verification via sequential posterior estimation , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Larry S. Davis,et al.  A probabilistic framework for rigid and non-rigid appearance based tracking and recognition , 2000, Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition (Cat. No. PR00580).

[6]  Michael J. Black,et al.  A probabilistic frameworkfor matching temporal trajectories , 1998, ICCV 1998.

[7]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

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

[9]  G. Kitagawa.  Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models , 1996 .

[10]  Michael Isard,et al.  Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.

[11]  Syed A. Rizvi,et al.  The FERET Evaluation , 1998 .

[12]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[13]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .