| Title | Robust sparse representation based face recognition in an adaptive weighted spatial pyramid structure |
| Authors | Ma, Xiao Zhang, Fandong Li, Yuelong Feng, Jufu |
| Affiliation | Peking Univ, Sch Elect Engn & Comp Sci, Beijing 100871, Peoples R China. Tianjin Polytech Univ, Sch Comp Sci & Software Engn, Tianjin 300387, Peoples R China. Univ York, Dept Comp Sci, York YO10 5GH, N Yorkshire, England. |
| Keywords | face recognition sparse representation self-adaptive weighted aggregating spatial pyramid structure local robust strategies CLASSIFICATION DICTIONARY FEATURES KERNEL |
| Issue Date | 2018 |
| Publisher | SCIENCE CHINA-INFORMATION SCIENCES |
| Citation | SCIENCE CHINA-INFORMATION SCIENCES. 2018, 61(1). |
| Abstract | 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. |
| URI | http://hdl.handle.net/20.500.11897/500276 |
| ISSN | 1674-733X |
| DOI | 10.1007/s11432-016-9009-6 |
| Indexed | SCI(E) EI |
| Appears in Collections: | 信息科学技术学院 |
