Face Recognition With Pose Variations and Misalignment via Orthogonal Procrustes Regression PROJECT TITLE :Face Recognition With Pose Variations and Misalignment via Orthogonal Procrustes RegressionABSTRACT:A linear regression-based method is a hot topic in face recognition community. Recently, sparse representation and collaborative representation-based mostly classifiers for face recognition have been proposed and attracted nice attention. However, most of the prevailing regression analysis-based mostly ways are sensitive to pose variations. During this paper, we tend to introduce the orthogonal Procrustes drawback (OPP) as a model to handle create variations existed in 2D face images. OPP seeks an optimal linear transformation between 2 pictures with completely different poses thus as to create the reworked image best fits the opposite one. We have a tendency to integrate OPP into the regression model and propose the orthogonal Procrustes regression (OPR) model. To handle the problem that the linear transformation isn't appropriate for handling highly non-linear pose variation, we have a tendency to additional adopt a progressive strategy and propose the stacked OPR. As a practical framework, OPR will handle face alignment, pose correction, and face representation simultaneously. We have a tendency to optimize the proposed model via an economical alternating iterative algorithm, and experimental results on three common face databases, such as CMU PIE database, CMU Multi-PIE database, and LFW database, demonstrate the effectiveness of our proposed technique. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Instance-Aware Hashing for Multi-Label Image Retrieval Patch-Based Video Denoising With Optical Flow Estimation