Coupled cascade regression for simultaneous facial landmark detection and head pose estimation

Current approaches for facial landmark detection and head pose estimation first perform landmark detection, followed by fitting 3D face model or regression model to estimate head pose. Different from the existing methods, in this paper, we propose a unified method, called Coupled Cascade Regression (CCR), for simultaneous facial landmark detection and head pose estimation. At each cascade level, two separate regressors are learned to update the landmark locations and 3D face model parameters based on the local appearance features, respectively. Since 2D facial landmark locations and head pose parameters are related, we further apply the projection model to refine the prediction results in each cascade iteration and make them consistent. As a result, CCR can leverage both the learning methods and the projection model to simultaneously perform facial landmark detection and pose estimation to enhance the performances of both tasks. Experimental results on 300-W and BU datasets indicate that our proposed CCR method outperforms many conventional methods both for landmark detection and head pose estimation.