CASCADED NONLINEAR SHAPE MODEL REGRESSION

This paper targets deformable face model matching in images using cascaded regression techniques. Recently, the cascaded regression strategies have became rather popular solutions to solve nonlinear objective functions by learning a pipeline sequence of linear regressors. However, despite their success, the standard formulation do not enforce shape consistency through the cascade (mostly because the individual regressors are learnt independently). In this paper we revisit the cascaded regression framework and propose a number of improvements. First we explore the simplicity and compactness of using a linear shape model for such tasks, effectively solving the previous drawback. Then we propose to extend the linear regression module into a nonlinear version, by means of recent Convolutional Neutral Networks (CNNs) techniques, modified to include a weighted shape aware loss function. Moreover, since CNNs often require massive amounts of data to perform well, we took advantage of the shape model probabilistic framework to efficiently bootstrap new data. Our nonlinear cascade regression method is evaluated in several databases (LFPW, LFW, HELEN and 300W), where the results demonstrate the effectiveness of the proposed method.