Head Pose Estimation of Occluded Faces using Regularized Regression
This addresses the problem of accurate head pose estimation in occluded faces for computer vision applications, but it appears incremental as it builds on existing regression methods with specific regularization techniques.
The paper tackles head pose estimation from occluded 2-D face images by using nuclear norm and LASSO regularization to improve reconstruction, achieving performance comparisons in accuracy.
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.