Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure  

作　　者：Xiao MA Fandong ZHANG Yuelong LI Jufu FENG 

机构地区：[1]School of Electronics Engineering and Computer Science, Peking University [2]School of Computer Science and Software Engineering, Tianjin Polytechnic University [3]Department of Computer Science, University of York

出　　处：《Science China(Information Sciences)》2018年第1期82-94,共13页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China (Grant Nos. 61333015, 61302127, 11326198);China Postdoctoral Science Foundation (Grant No. 2015M570228);Opening Foundation of Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology;Tianjin Key Projects in the National Science and Technology Pillar Program (Grant No. 14ZCZDGX00033);International Fostering Plan of Selected Excellent Postdoctors Subsidized by Tianjin City (2015)

摘　　要：The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches＇ reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure（RSRC-ASP） is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals＇ aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.The sparse representation based classification methods has achieved significant performance in recent years. To fully exploit both the holistic and locality information of face samples, a series of sparse representation based methods in spatial pyramid structure have been proposed. However, there are still some limitations for these sparse representation methods in spatial pyramid structure. Firstly, all the spatial patches in these methods are directly aggregated with same weights, ignoring the differences of patches＇ reliability. Secondly, all these methods are not quite robust to poses, expression and misalignment variations, especially in under-sampled cases. In this paper, a novel method named robust sparse representation based classification in an adaptive weighted spatial pyramid structure（RSRC-ASP） is proposed. RSRC-ASP builds a spatial pyramid structure for sparse representation based classification with a self-adaptive weighting strategy for residuals＇ aggregation. In addition, three strategies, local-neighbourhood representation, local intra-class Bayesian residual criterion, and local auxiliary dictionary, are exploited to enhance the robustness of RSRC-ASP. Experiments on various data sets show that RSRC-ASP outperforms the classical sparse representation based classification methods especially for under-sampled face recognition problems.

关 键 词：face recognition sparse representation self-adaptive weighted aggregating spatial pyramid structure local robust strategies 

分 类 号：TP4[自动化与计算机技术]