Abstract

Recognition of human faces using a gallery of still or video images and a probe set of videos is systematically investigated using a probabilistic framework. In still-to-video recognition, where the gallery consists of still images, a time series state space model is proposed to fuse temporal information in a probe video, which simultaneously characterizes the kinematics and identity using a motion vector and an identity variable, respectively. The joint posterior distribution of the motion vector and the identity variable is estimated at each time instant and then propagated to the next time instant. Marginalization over the motion vector yields a robust estimate of the posterior distribution of the identity variable. A computationally ecient sequential importance sampling algorithm is developed to provide a numerical solution to the model. Theoretical derivations under weak assumptions demonstrate that, due to the propagation of the identity variable over time, a degeneracy in the posterior probability of the identity variable is exploited to give improved recognition.

Citations