Learning faces: similar comparator faces do not improve performance

A Method for Non-Rigid Face Alignment via Combining Local and Holistic Matching

We propose a method for non-rigid face alignment which only needs a single template, such as using a person’s smile face to match his surprise face. First, in order to be robust to outliers caused by complex geometric deformations, a new local feature matching method called K Patch Pairs (K-PP) is proposed. Specifically, inspired by the state-of-art similarity measure used in template matching, K-PP is to find the mutual K nearest neighbors between two images. A weight matrix is then presented to balance the similarity and the number of local matching. Second, we proposed a modified Lucas-Kanade algorithm combined with local matching constraint to solve the non-rigid face alignment, so that a holistic face representation and local features can be jointly modeled in the object function. Both the flexible ability of local matching and the robust ability of holistic fitting are included in our method. Furthermore, we show that the optimization problem can be efficiently solved by the inverse compositional algorithm. Comparison results with conventional methods demonstrate our superiority in terms of both accuracy and robustness.