Pairwise linear regression: An efficient and fast multi-view facial expression recognition

Multi-view facial expression recognition (MFER) is an active research topic in facial analysis. In fact, not only the accuracy but also time complexity is desirable for real applications. In this paper, we introduce a new fast and robust approach for recognizing facial expressions in arbitrary views. Our approach relies on learning linear regressions between pairs of non-frontal and frontal sets to virtually compensate occluded facial parts. We learn linear regression for projecting from non-frontal to frontal views. Such approximated frontal training features are applied for training view specific facial expression classifiers. We propose a number of different variants of our approach, including sparse encoding and ridge-regression for feature representation. While classical pose specific methods strongly depend on the quality of the pose estimation step, our approaches maintain their superior behavior even under severe pose noise. We evaluate on both BU3DFE and Multi-PIE datasets and outperform the state-of-the-art in classification accuracy, even with a simple pose specific baseline method, while being extremely robust to feature noise and erroneous viewpoint estimation with our pairwise regression approaches.