Nuclear Norm based Matrix Regression with Applications to Face Recognition with Occlusion and Illumination Changes
This addresses face recognition robustness for applications like security, but it is incremental as it builds on existing regression methods by incorporating structural error modeling.
The paper tackled face recognition under occlusion and illumination changes by proposing a nuclear norm based matrix regression model that uses low-rank structural information of error images, achieving performance advantages over state-of-the-art regression methods on four face databases.
Recently regression analysis becomes a popular tool for face recognition. The existing regression methods all use the one-dimensional pixel-based error model, which characterizes the representation error pixel by pixel individually and thus neglects the whole structure of the error image. We observe that occlusion and illumination changes generally lead to a low-rank error image. To make use of this low-rank structural information, this paper presents a two-dimensional image matrix based error model, i.e. matrix regression, for face representation and classification. Our model uses the minimal nuclear norm of representation error image as a criterion, and the alternating direction method of multipliers method to calculate the regression coefficients. Compared with the current regression methods, the proposed Nuclear Norm based Matrix Regression (NMR) model is more robust for alleviating the effect of illumination, and more intuitive and powerful for removing the structural noise caused by occlusion. We experiment using four popular face image databases, the Extended Yale B database, the AR database, the Multi-PIE and the FRGC database. Experimental results demonstrate the performance advantage of NMR over the state-of-the-art regression based face recognition methods.