Face Recognition in Videos Using Adaptive Graph Appearance Models

In this paper, we present a novel graph, sub-graph and supergraph based face representation which captures the facial shape changes and deformations caused due to pose changes and use it in the construction of an adaptive appearance model. This work is an extension of our previous work proposed in [1]. A sub-graph and super-graph is extracted for each pair of training graphs of an individual and added to the graph model set and used in the construction of appearance model. The spatial properties of the feature points are effectively captured using the graph model set. The adaptive graph appearance model constructed using the graph model set captures the temporal characteristics of the video frames by adapting the model with the results of recognition from each frame during the testing stage. The graph model set and the adaptive appearance model are used in the two stage matching process, and are updated with the sub-graphs and super-graphs constructed using the graph of the previous frame and the training graphs of an individual. The results indicate that the performance of the system is improved by using sub-graphs and super-graphs in the appearance model.