Person recognition using facial video information: A state of the art

In this article we propose a detailed state of the art on person recognition using facial video information. We classify the existing approaches present in the scientific literature between those that neglect the temporal information, and those that exploit it even partially. Concerning the first category, we detail the extensions to video data of: eigenfaces, fisherfaces, active appearance models (AAMs), radial basis function neural networks (RBFNNs), elastic graph matching (EGM), hierarchical discriminative regression trees (HDRTs) and pairwise clustering methods. After that, we focus on the strategies exploiting the temporal information, in particular those analysing: the facial motion with optical flow, the evolution of facial appearance over time with hidden Markov models (HMMs) or with various probabilistic tracking and recognition approaches, and the head motion with Gaussian mixture models.