3-D Face Modeling from a 2-D Image with Shape and Head Pose Estimation

Abstract

This paper presents 3-D face modeling with head pose and depth information estimated from a 2-D query face image. Many recent approaches to 3-D face modeling are based on a 3-D morphable model that separately encodes the shape and texture in a parameterized model. The model parameters are often obtained by applying statistical analysis to a set of scanned 3-D faces. Such approaches tend to depend on the number and quality of scanned 3-D faces, which are difficult to obtain and computationally intensive. To overcome the limitations of 3-D morphable models, several modeling techniques from 2-D images have been proposed. We propose a novel framework for depth estimation from a single 2-D image with an arbitrary pose. The proposed scheme uses a set of facial features in a query face image and a reference 3-D face model to estimate the head pose angles of the face. The depth information of the subject at each feature point is represented by the depth information of the reference 3-D face model multiplied by a vector of scale factors. We use the positions of a set of facial feature points on the query 2-D image to deform the reference face dense model into a person specific 3-D face by minimizing an objective function. The objective function is defined as the feature disparity between the facial features in the face image and the corresponding 3-D facial features on the rotated reference model projected onto 2-D space. The pose and depth parameters are iteratively refined until stopping criteria are reached. The proposed method requires only a face image of arbitrary pose for the reconstruction of the corresponding 3-D face dense model with texture. Experiment results with USF Human-ID and Pointing'04 databases show that the proposed approach is effective to estimate depth and head pose information with a single 2-D image.