Robust infants face tracking using active appearance models: a mixed-state condensation approach

Authors:
Luigi Bagnato;Matteo Sorci;Gianluca Antonini;Giuseppe Baruffa;Andrea Maier;Peter Leathwood;Jean-Philippe Thiran
Affiliations:
Ecole Polytechnique Federale de Lausanne, Signal Processing Institute, Lausanne, Switzerland;Ecole Polytechnique Federale de Lausanne, Signal Processing Institute, Lausanne, Switzerland;Ecole Polytechnique Federale de Lausanne, Signal Processing Institute, Lausanne, Switzerland;University of Perugia, Dept.of Electronic and Information Engineering, Perugia, Italy;Nestlé Research Center, Food-Consumer Interactions Department, Lausanne, Switzerland;Nestlé Research Center, Food-Consumer Interactions Department, Lausanne, Switzerland;Ecole Polytechnique Federale de Lausanne, Signal Processing Institute, Lausanne, Switzerland
Venue:
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part I
Year:
2007

Citing 3
Cited 0

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Abstract

In this paper a new extension of the CONDENSATION algorithm, with application to infants face tracking, will be introduced. In this work we address the problem of tracking a face and its features in baby video sequences. A mixed state particle filtering scheme is proposed, where the distribution of observations is derived from an active appearance model. The mixed state approach combines several dynamic models in order to account for different occlusion situations. Experiments on real video show that the proposed approach augments the tracker robustness to occlusions while maintaining the computational time competitive.