Nonlinear deformation learning for face alignment across expression and pose

As a fundamental work for the automatic emotional health analysis, we present a non-linear deformation learning approach to align face images and extract feature points undergoing a variety of expression and pose variations. To face the application in practice, only a single template of the query face is offered for the proposed method. Accurate alignment depends on estimating the optimal deformable shape parameters. We find that facial deformation characters can constrain the shape parameters indirectly. By formulating the non-linear deformation as several piece-wise convex combinations of local neighbor samples, the deformation constraint is imposed to the objective function. An adaptive optimization method is presented, in which the local neighbors are updated correspondingly. Our fitting model satisfies both the global shape prior and the deformation correlations among all feature points. Moreover, to avoid the optimization stacking into local minimal, a discriminative method is further designed to guide the facial deformation in each iterative search. Thus the proposed method is suitable for fully automatic applications. Extensive experiments demonstrate the accuracy and effectiveness of our approach.