Simultaneous tracking and recognition of human faces from video

The paper investigates the interaction between tracking and recognition of human faces from video under a framework proposed earlier (Shaohua Zhou et al., Proc. 5th Int. Conf. on Face and Gesture Recog., 2002; Shaohua Zhou and Chellappa, R., Proc. European Conf. on Computer Vision, 2002), 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 frames and between the observation and 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.