Robust linear representation via exploiting structure prior

Over the past few years, linear representation models have seen a lot of successful applications such as face recognition in computer vision. In the context of face recognition, occlusion is a key factor that often curbs the performance of practical face recognition systems. In this paper, we propose to alleviate such negative influence of the occlusion noises by explicitly encoding the spatial continuity prior of the occlusion. Given the fact that many real-world occlusions such as sunglasses and scarves are contiguous, taking such prior into account can help build a more accurate model and achieve higher recognition rates. Besides, a general framework has also been proposed in which many off-the-shelf linear representation models can be nicely incorporated. And the minimization objectives of all these models can be solved via the same Half-Quadratic optimization procedure. Therefore the robustness of these models to occlusions can be comprehensively evaluated on a fair platform. Extensive experiments on the AR and Extended Yale B face databases corroborate that the proposed algorithms can improve the model robustness to contiguous occlusions.