Mixture of related regressions for head pose estimation

Mixture of regressions is one of the most well-known statistical techniques for the problem of head pose estimation. However, conventional approaches are often sensitive to noise and suffer from underdetermined problem when the training data is insufficient (i.e., the number of training samples for some regressors is less than the dimensionality of the image features). In this paper, we propose a novel approach, named mixture of related regressions (MReR) to address above limitations. By imposing an additional similarity constraint on related regressors, MReR can significantly enhance robustness and avoid uncertainty for head pose estimation. As a nontrivial byproduct, we also develop an EM-type algorithm to efficiently solve the MReR model. Experimental results on both synthetic and real-world datasets demonstrate the benefits of MReR.