Unconstrained pose-invariant face recognition by a triplet collaborative dictionary matrix

Highlight
• We propose a novel method for real-world pose-invariant face recognition.
• Proposed method used from a single image in gallery with any facial expressions.
• We generate a collaborative dictionary matrix for each people.
• Promising results were obtained to handle pose on the FERET, LFW and video databases.

In this paper, a novel method is proposed for unconstrained pose-invariant face recognition from only an image in a gallery. A 3D face is initially reconstructed using only a 2D frontal image. Then, for each person in the gallery, a Triplet Collaborative Dictionary Matrix (TCDM) is created from all face poses by rotating the 3D reconstructed models and extracting features in rotated face. Each TCDM is subsequently rendered based on triplet angles of face poses. Finally, the classification is performed by Collaborative Representation Classification (CRC) with Regularized Least Square (RLS). Promising results were acquired to handle pose changes on the FERET, LFW and video face databases compared to state-of-the-art methods in pose-invariant face recognition.