Authors:
Fernando De la Torre
;
Joan Campoy
;
Jeffrey F. Cohn
and
Takeo Kanade
Affiliation:
Robotics Institute, Carnegie Mellon University, United States
Keyword(s):
Facial expression analysis, Clustering, Facial Gesture, Learning, Temporal segmentation.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Soft Computing
Abstract:
Temporal segmentation of facial gestures from video sequences is an important unsolved problem for automatic facial analysis. Recovering temporal gesture structure from a set of 2D facial features tracked points is a challenging problem because of the difficulty of factorizing rigid and non-rigid motion and the large variability in the temporal scale of the facial gestures. In this paper, we propose a two step approach for temporal segmentation of facial gestures. The first step consist on clustering shape and appearance features into a number of clusters and the second step involves temporally grouping these clusters. Results on clustering largely depend on the registration process. To improve the clustering/registration, we propose a Parameterized Cluster Analysis (PaCA) method that jointly performs registration and clustering. Besides the joint clustering/registration, PaCA solves the rounding off problem of existing spectral graph methods for clustering. After the clustering is p
erformed, we group sets of clusters into facial gestures. Several toy and real examples show the benefits of our approach for temporal facial gesture segmentation.
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