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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...
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