  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.
