| Bayesian-based performance
prediction for gait recognition
Presented by: Ju Han
ABSTRACT: Existing gait recognition approaches do not give
their theoretical or experiential performance predictions.
Therefore, the discriminating power of gait as a feature for
human recognition cannot be evaluated. In this paper, we first
propose a kinematic-based approach to recognize human by gait.
The proposed approach estimates 3D human walking parameters
by performing a least squares fit of the 3D kinematic model
to the 2D silhouette extracted from a monocular image sequence.
Next, a Bayesian based statistical analysis is performed to
evaluate the discriminating power of extracted features. Through
probabilistic simulation, we not only predict the probability
of correct recognition (PCR) with regard to different within-class
feature variance, but also obtain the upper bound on PCR with
regard to different human silhouette resolution. In addition,
the maximum number of people in a database is obtained given
the allowable error rate. This is extremely important for
gait recognition in large databases.