Real-Time Pose-Invariant Face Recognition by Triplet Pose Sparse Matrix from Only a Single Image

In this paper, a novel method for real-time pose-invariant face recognition is proposed from only a single image in a gallery including any facial expressions. A 3D Facial Expression Generic Elastic Model (3D FE-GEM) is proposed to reconstruct 3D model of each human face in the present database using only a single 2D frontal image. Then, for each person in the database, a Triplet Pose Sparse Matrix (TPSM) is created from all face poses by rotating the 3D reconstructed models and extracting features in rotated face. Each TPSM is subsequently rendered based on triplet angles of face poses. Before matching to TPSM, an initial estimate of triplet angles of face poses is obtained in the test face image/video using an automatic head pose estimation approach. Then, an array of the TPSM is selected based on the estimated triplet angles for each subject. Finally, the selected arrays from TPSMs are compared with target image by joint dynamic sparse representation classification. Favorable outcomes were acquired to handle pose and expression changes on the available image and video databases based on the proposed method compared to several state-of-the-arts in pose-invariant face recognition.