Table of Contents

This article has been retracted, upon the authors’ request, as they found a bug in the Matlab code used in this study which makes the results incorrect.

View the full Retraction here.


  1. S. Zhao and Z. Hu, “Occluded face recognition based on double layers module sparsity difference,” Advances in Electronics, vol. 2014, Article ID 687827, 6 pages, 2014.
Advances in Electronics
Volume 2014, Article ID 687827, 6 pages
Research Article

Occluded Face Recognition Based on Double Layers Module Sparsity Difference

School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China

Received 14 May 2014; Revised 5 August 2014; Accepted 6 August 2014; Published 18 August 2014

Academic Editor: Durga Misra

Copyright © 2014 Shuhuan Zhao and Zheng-ping Hu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Image recognition with occlusion is one of the popular problems in pattern recognition. This paper partitions the images into some modules in two layers and the sparsity difference is used to evaluate the occluded modules. The final identification is processed on the unoccluded modules by sparse representation. Firstly, we partition the images into four blocks and sparse representation is performed on each block, so the sparsity of each block can be obtained; secondly, each block is partitioned again into two modules. Sparsity of each small module is calculated as the first step. Finally, the sparsity difference of small module with the corresponding block is used to detect the occluded modules; in this paper, the small modules with negative sparsity differences are considered as occluded modules. The identification is performed on the selected unoccluded modules by sparse representation. Experiments on the AR and Yale B database verify the robustness and effectiveness of the proposed method.