Name
Abstract | ||
|---|---|---|
In this paper, we propose a novel noise modeling framework to improve a representation based classification (NMFIRC) method for robust face recognition. The representation based classification method has evoked large repercussions in the field of face recognition. Generally, the representation based classification method (RBCM) always first represents the test sample as a linear combination of the training samples, and then classifies the test sample by judging which class leads to a minimum reconstruction residual. However, RBCMs still cannot ideally resolve the face recognition problem owing to the varying facial expressions, poses and different illumination conditions. Furthermore, these variations can immensely influence the representation accuracy when using RBCMs to perform classification. Thus, it is a crucial problem to explore an effective way to better represent the test sample in RBCMs. In order to obtain a highly precise representation metric, the proposed framework first iteratively diminishes the representation noise and achieves better representation solution of the linear combination until it converges, and then exploits the determined ‘optimal’ representation solution and a fusion method to perform classification. Extensive experiments demonstrated that the proposed framework can simultaneously notably improve the representation capability by decreasing the representation noise and improve the classification accuracy of RCBM. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.neucom.2014.07.058 | Neurocomputing |
Keywords | Field | DocType |
Pattern recognition,Face recognition,Noise modeling,Representation based method | Linear combination,Residual,Facial recognition system,Computer vision,Pattern recognition,Facial expression,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | ISSN | Citations |
148 | 0925-2312 | 8 |
PageRank | References | Authors |
0.45 | 24 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zheng Zhang | 1 | 549 | 40.45 |
| Long Wang | 2 | 93 | 9.09 |
| Qi Zhu | 3 | 147 | 11.68 |
| Zhonghua Liu | 4 | 115 | 11.12 |
| Yan Chen | 5 | 23 | 1.30 |