Robust and discriminative dictionary learning for face recognition

Abstract

For face recognition, conventional dictionary learning (DL) methods have some disadvantages. First, face images of the same person vary with facial expressions and pose, illumination and disguises, so it is hard to obtain a robust dictionary for face recognition. Second, they don’t cover important components (e.g., particularity and disturbance) completely, which limit their performance. In the paper, we propose a novel robust and discriminative DL (RDDL) model. The proposed model uses sample diversities of the same face image to learn a robust dictionary, which includes class-specific dictionary atoms and disturbance dictionary atoms. These atoms can well represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which improves effectively the classification capability of the dictionary. The proposed RDDL is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

A weighted block cooperative sparse representation algorithm based on visual saliency dictionary

“…The traditional methods almost adopted wavelet‐based dictionaries [2, 3], which have a single dictionary atom and are not suitable for specific tasks such as face recognition, image classification and image reconstruction. The effect of sparse representation classification in practical application can be greatly improved by optimising the compact and discriminant dictionary through constraint learning of training samples.…”