Face Alignment With Expression- and Pose-Based Adaptive Initialization

Face alignment is a critical task in many multimedia and vision applications that use face-based algorithms. Recent research has focused on achieving efficient initialization to improve performance; however, the use of facial attributes and the extent of their correlation with initialization have not been fully exploited. This paper presents a lightweight method called expression- and pose-based adaptive initialization (EXPAI), in which facial attributes, that is, expression and pose information, are used as priors. This approach can significantly improve the face alignment performance. In addition, reliable expression and head pose information can be derived simultaneously in the same framework. First, an expression- and pose-based template dictionary is formed by augmenting the mean shape across three degrees of freedom, thereby substantially improving the robustness of the initial shape with respect to large head pose variations. Second, each the template corresponds to an image of interest, which is jointly determined using a shape-constrained multiclass classifier and binary classifiers, and is assigned a pretrained confidence coefficient. The initial shape that is thus generated for subsequent cascaded regression is more adaptive and enables higher accuracy. Furthermore, EXPAI enables initialization with significantly increased computational efficiency because of its independence from the original dataset. The experimental results obtained on the widely used 300-W dataset show that our method achieves very competitive performance compared with that of state-of-the-art methods. In particular, for the challenging subset of 300-W, EXPAI reduces errors by more than 14% compared with coarse-to-fine shape searching (CFSS), which currently exhibits the best performance among regression-based approaches. Furthermore, a speed increase of more than 10 times compared with CFSS is achieved.