@inproceedings{0c2399a3a12d4efd953d19538f756acd,
title = "Structured regularized robust coding for face recognition",
abstract = "The sparse representation based classifier (SRC) has been successfully applied to robust face recognition (FR) with various variations. To achieve much stronger robustness to facial occlusion, recently regularized robust coding (RRC) was proposed by designing a new robust representation residual term. Although RRC has achieved the leading performance, it ignores the structured information (i.e., spatial consistence) embedded in the occluded pixels. In this paper, we proposed a novel structured regularized robust coding (SRRC) framework, in which the spatial consistence of occluded pixels was exploited by pixel weight learning (PWL) model. Efficient algorithms were also proposed to fastly learn the pixel{\textquoteright}s weight and accurately recover the occluded area. The experiments on face recognition in several representative datasets clearly show the advantage of the proposed SRRC in accuracy and efficiency.",
keywords = "Face recognition, Robust coding, Structure regularized",
author = "Meng Yang and Tiancheng Song and Feng Liu and Linlin Shen",
note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2015.; 1st Chinese Conference on Computer Vision, CCCV 2015 ; Conference date: 18-09-2015 Through 20-09-2015",
year = "2015",
doi = "10.1007/978-3-662-48570-5_9",
language = "English",
isbn = "9783662485699",
series = "Communications in Computer and Information Science",
publisher = "Springer Verlag",
pages = "80--89",
editor = "Liang Wang and Hongbin Zha and Xilin Chen and Qiguang Miao",
booktitle = "Computer Vision CCF Chinese Conference, CCCV 2015, Proceedings",
address = "Germany",
}