Robust head pose estimation via Convex Regularized Sparse Regression

This paper studies the problem of learning robust regression for real world head pose estimation. The performance and applicability of traditional regression methods in real world head pose estimation are limited by a lack of robustness to outlying or corrupted observations. By introducing low- rank and sparse regularizations, we propose a novel regression method, named Convex Regularized Sparse Regression (CRSR), for simultaneously removing the noise and outliers from the training data and learning the regression between image features and pose angles. We verify the efficiency of the proposed robust regression method with extensive experiments on real data, demonstrating lower error rates and efficiency than existing methods.