Simultaneous tracking and recognition of human faces from video

This paper investigates the interaction between tracking and recognition of human faces from video under the framework proposed earlier [1, 2], where a time series model is used to resolve the uncertainties in both tracking and recognition. However, our earlier efforts employed only a simple likelihood measurement in the form of a Laplacian density to deal with appearance changes between the frames and between the observation and the gallery images, yielding poor accuracies in both tracking and recognition when confronted by pose and illumination variations. The interaction between tracking and recognition was not well understood. We address the interdependence between tracking and recognition using a series of experiments and quantify the interacting nature of tracking and recognition.