Face Recognition by Elastic Bunch Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICAPR '01 Proceedings of the Second International Conference on Advances in Pattern Recognition
Bayesian face recognition using deformable intensity surfaces
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Probabilistic recognition of human faces from video
Computer Vision and Image Understanding - Special issue on Face recognition
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Video Database of Moving Faces and People
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Feature Matching For Face Recognition
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Journal of Cognitive Neuroscience
Visual tracking and recognition using probabilistic appearance manifolds
Computer Vision and Image Understanding
Video-based face recognition using adaptive hidden markov models
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Video based face recognition using graph matching
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
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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 subgraphs and super-graphs in the appearance model.