Multi-resolution dictionary collaborative representation for face recognition

Zhen Liu

doi:10.1007/s10044-021-00987-9

Return

Multi-resolution dictionary collaborative representation for face recognition

2021-08-21

PRE

Zhen Liu

Xiao‐Jun Wu *

舒振球 (Zhenqiu Shu)

DOI：10.1007/s10044-021-00987-9

Original

Original request for help

Save

Abstract

En 中文

In this paper, a multi-resolution dictionary collaborative representation(MRDCR) method for face recognition is proposed. Unlike most of the traditional sparse learning methods, such as sparse representation-based classification(SRC) methods and dictionary learning(DL)-based methods, which concentrate only on a single resolution, we consider the fact that the resolutions of real-world face images are variable. We use multiple dictionaries each being related with a resolution to collaboratively represent the test image. Main advantages of this work are summarized as follows. First, we extend the traditional collaborative representation-based classification(CRC) method to the multi-resolution dictionary case, which obtains better recognition accuracy than traditional SRC/CRC methods. Second, comparing with conventional DL methods and recently proposed multi-resolution dictionary learning(MRDL) method, MRDCR still shows superior performance, even in the case of random baboon block occlusion. Third, on the small-scale face databases, our method has achieved better results than some deep learning methods. Last, MRDCR has a closed-form solution, which makes it more efficient than most of the traditional sparse learning methods. The experimental results on five benchmark face databases and a Virus database demonstrate that our proposed MRDCR method outperforms many state-of-the-art dictionary learning and sparse representation methods. The MATLAB code will be available at littps://github.com/masterliuhzen/.

Keywords:

Multi-resolution dictionary collaborative representation

Collaborative representation

Multi-resolution dictionary

face recognition

AI Summary

Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.