Abstract.
In this paper, we propose a two-level integrated model for accurate face shape alignment. At the low level, the shape is split into a set of line segments which serve as the nodes in the hidden layer of a Markov Network. At the high level, all the line segments are con- strained by a global Gaussian point distribution model. Furthermore, those already accurately aligned points from the low level are detected and constrained using a constrained regularization algorithm. By analyz- ing the regularization result, a mask image of local minima is generated to guide the distribution of Markov Network states, which makes our al- gorithm more robust. Extensive experiments demonstrate the accuracy and effectiveness of our proposed approach.