Head Pose Estimation of Occluded Faces using Regularized Regression

TL;DR

This paper introduces nuclear norm regularized regression methods for head pose estimation from occluded face images, effectively handling contiguous occlusion errors to improve accuracy.

Contribution

It proposes a novel approach combining nuclear norm and LASSO regularization for better face reconstruction under occlusion, enhancing head pose estimation accuracy.

Findings

Nuclear norm regularization captures error structure better than pixel-wise assumptions.

The proposed method outperforms traditional reconstruction techniques in accuracy.

Reconstruction with nuclear norm improves head pose estimation on occluded faces.

Abstract

This paper presents regression methods for estimation of head pose from occluded 2-D face images. The process primarily involves reconstructing a face from its occluded image, followed by classification. Typical methods for reconstruction assume that the pixel errors of the occluded regions are independent. However, such an assumption is not true in the case of occlusion, because of its inherent contiguous nature. Hence, we use nuclear norm as a metric that can describe well the structure of the error. We also use LASSO Regression based l1 - regularization to improve reconstruction. Next, we implement Nuclear Norm Regularized Regression (NR), and also our proposed method, for reconstruction and subsequent classification. Finally, we compare the performance of the methods in terms of accuracy of head pose estimation of occluded faces.