New Robust Regression Method for Outliers and Heavy Sparse Noise Detection via Affine Transformation for Head Pose Estimation and Image Reconstruction in Highly Complex and Correlated Data: Applications in Signal Processing

Abstract

In this work, we propose a novel method for head pose estimation and face recovery, particularly to solve the potential impacts of noises in signal processing to get an efficient and effective model that is more resilient with annoying effects through adding affine transformation with the low-rank robust subspace regression. Consequently, the corrupted images can be correctly recovered by affine transformations to render more best regression outcomes. Thereby, we need to search so as to get optimal parameters which can be regarded as convex constrained optimization techniques. Afterward, the alternating direction method for multipliers (ADMM) approach is considered and a new set of updated equations is well established so as to update the optimization parameters and affine transformations iteratively in a round-robin manner. Additionally, the convergence of these new updating equations is well scrutinized as well. Thus, the experimental simulations reveal that the proposed method outperforms the state-of-the-art works for head pose estimation and face recovery on some public databases.

An Efficient New Robust PCA Method for Joint Image Alignment and Reconstruction via the L 2,1 Norms and Affine Transformation

“…(4) e L 2,1 norm of G is taken to estimate the Gaussian noise in real applications so as to minimize the potential impact of outliers, occlusions and illuminations, and heavy sparse errors. Unlike to the others work [2,[33][34][35][36][37][38][39], the new method tries to decompose the aggregated errors as the Gaussian noise and sparse error, which make the new method more novel. (5) e new set of equations, which are derived in more detailed including affine transformation using an ADMM method, is used to improve the robustness and solve the set of new optimization problems.…”