An Efficient Three-Dimensional Reconstruction Approach for Pose-Invariant Face Recognition Based on a Single View

摘要

A three-dimensional (3D) reconstruction approach based on a single view is proposed to solve the problem of lack of training samples while addressing multi-pose face recognition. First, a planar template is defined based on the geometric information of the segmented faces. Second, 3D faces are resampled according to the geometric relationship between the planar template and original 3D faces, and a normalized 3D face database is obtained. Third, a 3D sparse morphable model is established based on the normalized 3D face database, and a new 3D face can be reconstructed from a single face image. Lastly, virtual multi-pose face images can be obtained by texture mapping, rotation, and projection of the established 3D face, and training samples are enriched. Experimental results obtained using BJUT-3D and CAS-PEAL-R1 face databases show that recognition rate of the proposed method is 91%, which is better than other methods for pose-invariant face recognition based on a single view. This is primarily because the training samples are enriched using the proposed 3D sparse morphable model based on a new dense correspondence method.