With one look: 3D face shape estimation from a single snapshot

Estimating the 3D shape information of a face from a single image is a challenging task, especially when the input image is captured under unconstrained scenarios (e.g., variations of pose, illumination, expression, or even disguise). Previous approaches to this problem typically require careful initialization, registration, or segmentation of the face image regions. With the objective to match the detected landmarks of the input image with those of a set of reference 3D models, we propose a non-negative least squares (NNLS) based algorithm for joint pose and shape estimation. With the additional imposed pose regularization, our method is able to perform person-specific shape estimation, while the camera pose can be simultaneously recovered. We show that our method is robust, effective, and computationally feasible. Moreover, it would perform favorably against existing approaches to 3D shape estimation from a single unconstrained image.