Improving the Dictionary Construction in Sparse Representation using PCANet for Face Recognition

Abstract

Recently, sparse representation has attracted increasing interest in computer vision. Sparse representation based methods, such as sparse representation classification (SRC), have produced promising results in face recognition, while the dictionary used for sparse representation plays a key role in it. How to improve the dictionary construction in sparse representation is still an open question. Principal component analysis network (PCANet), as a newly proposed deep learning method, has the advantage of simple network architecture and competitive performance for feature learning. In this paper, we have studied how to use the PCANet to improve the dictionary construction in sparse representation, and proposed a new method for face recognition. The PCANet is used to learn new features from face images, and the learned features are used as dictionary atoms to code the query face images, and then the reconstruction errors after sparse coding are used to classify the face images. It is shown that the proposed method can achieve better performance than the other five state-of-art methods for face recognition.