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Robust facial expression tracking based on composite constraints AAM

Facial expression tracking is a challenging task because the head pose may change in a large range and the expression is highly non-rigid. It can be formulized as an energy minimization problem. The two most important issues are the construction of the cost function and the selection of the initial value. In this paper, we present a fast and robust expression tracking algorithm called Composite Constraints AAM. Firstly, a novel cost function is proposed to enhance the convergence by combining multiple constraints in a unified framework. Secondly, widely used local features are strictly tested with face videos, and an efficient motion estimation method is presented to provide a good initial value to the iterative optimization process. Experimental result demonstrates that our system can track the head pose and facial expression with very high stability in real time speed.