Name
Abstract | ||
|---|---|---|
Tensor analysis methods have played an important role in identifying human gaits using high dimensional data. However, when view angles change, it becomes more and more difficult to recognize cross-view gait by learning only a set of multi-linear projection matrices. To address this problem, a general tensor representation framework for cross-view gait recognition is proposed in this paper. There are three criteria of tensorial coupled mappings in the proposed framework. (1) Coupled multi-linear locality-preserved criterion (CMLP) aims to detect the essential tensorial manifold structure via preserving local information. (2) Coupled multi-linear marginal fisher criterion (CMMF) aims to encode the intra-class compactness and inter-class separability with local relationships. (3) Coupled multi-linear discriminant analysis criterion (CMDA) aims to minimize the intra-class scatter and maximize the inter-class scatter. For the three tensor algorithms for cross-view gaits, two sets of multi-linear projection matrices are iteratively learned using alternating projection optimization procedures. The proposed methods are compared with the recently published cross-view gait recognition approaches on CASIA(B) and OU-ISIR gait database. The results demonstrate that the performances of the proposed methods are superior to existing state-of-the-art cross-view gait recognition approaches. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.patcog.2019.01.017 | Pattern Recognition |
Keywords | Field | DocType |
Gait recognition,Cross-view gait,Tensor representation,Framework | ENCODE,Clustering high-dimensional data,Pattern recognition,Gait,Tensor,Matrix (mathematics),Compact space,Artificial intelligence,Linear discriminant analysis,Mathematics,Tensor representation | Journal |
Volume | Issue | ISSN |
90 | 1 | 0031-3203 |
Citations | PageRank | References |
9 | 0.51 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xianye Ben | 1 | 131 | 10.56 |
| Peng Zhang | 2 | 18 | 2.34 |
| Zhihui Lai | 3 | 1204 | 76.03 |
| Rui Yan | 4 | 88 | 5.22 |
| Xinliang Zhai | 5 | 9 | 0.51 |
| Wei-Xiao Meng | 6 | 151 | 32.05 |