A fast Cascade Shape Regression Method based on CNN-based Initialization

Cascade shape regression (CSR) methods predict facial landmarks by iteratively updating an initial shape and are state-of-the-art. The initial shape always limits the result and causes local optimum, which is usually obtained from the average face or by randomly picking a face from the training set. In this paper, we propose a CNN-based initial method for CSR. Convolution neural network provides a highly robust initial shape estimation, while the following CSR algorithm fine-tunes the initialization rapidly to achieve higher accuracy. Furthermore, CNN-based initial approach is proposed to get 68-point initial shape, which is calculated from convolutional network 5-point result by the radial basis function interpolation with thin-plate splines (RBF-TPS). Extensive experiments demonstrate that CSR methods are sensitive to the initialization and proposed approach gets favorable results compared to state-of-the-art algorithms and achieves real-time performance.