Real-World and Rapid Face Recognition Toward Pose and Expression Variations via Feature Library Matrix - 2015


In this paper, a unique method for face recognition beneath create and expression variations is proposed from only one image in the gallery. A 3D probabilistic countenance recognition generic elastic model is proposed to reconstruct a 3D model from real-world human face using solely one 2D frontal image with/without facial expressions. Then, a feature library matrix (FLM) is generated for every subject in the gallery from all face poses by rotating the 3D reconstructed models and extracting options within the rotated face pose. So, each FLM is subsequently rendered for every subject within the gallery primarily based on triplet angles of face poses. Furthermore, before matching the FLM, an initial estimate of triplet angles is obtained from the face pose in probe pictures using an automatic head create estimation approach. Then, an array of the FLM is chosen for each subject primarily based on the estimated triplet angles. Finally, the chosen arrays from FLMs are compared with extracted features from the probe image by iterative scoring classification using the support vector machine. Convincing results are acquired to handle pose and expression changes on the Bosphorus, Face Recognition Technology (FERET), Carnegie Mellon University-Pose, Illumination, and Expression (CMU-PIE), and Labeled Faces in the Wild (LFW) face databases compared with many state-of-the-art ways in create-invariant face recognition. The proposed methodology not only demonstrates an wonderful performance by getting high accuracy on all four databases but additionally outperforms other approaches realistically.

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