Augmented Sparse Representation Classifier for Blurred Face Recognition

The sparse representation classifier (SRC) has been developed to offer a formulation to the face recognition problem under scene dependent conditions, such as illumination/pose variations, occlusion, and disguise. However, this method has not considered image quality degradations resulting from capture, such as blur, under the same scene variations. In this work, we explore the performance of the well-known face recognition framework SRC in the presence of Gaussian blur in both constrained and unconstrained environments. Finally, we propose an augmented SRC (ASRC) framework to improve the performance of the original SRC in the presence of Gaussian blur, while preserving its robustness to scene dependent variations.