Low-rank matrix recovery with structural incoherence for robust face recognition

We address the problem of robust face recognition, in which both training and test image data might be corrupted due to occlusion and disguise. From standard face recog-nition algorithms such as Eigenfaces to recently proposed sparse representation-based classification (SRC) methods, most prior works did not consider possible contamination of data during training, and thus the associated performance might be degraded. Based on the recent success of low-rank matrix recovery, we propose a novel low-rank matrix ap-proximation algorithm with structural incoherence for ro-bust face recognition. Our method not only decomposes raw training data into a set of representative basis with corre-sponding sparse errors for better modeling the face images, we further advocate the structural incoherence between the basis learned from different classes. These basis are en-couraged to be as independent as possible due to the regu-larization on structural incoherence. We show that this pro-vides additional discriminating ability to the original low-rank models for improved performance. Experimental re-sults on public face databases verify the effectiveness and robustness of our method, which is also shown to outper-form state-of-the-art SRC based approaches. 1

Sparse representation for face images.

This thesis address issues for face recognition with multi-view face images. Several effective methods are proposed and compared with current state of the art. A novel framework that generalises existing sparse representation-based methods in order to exploit the sharing information to against pose variations of face images is proposed