Name
Abstract | ||
|---|---|---|
This paper presents a novel method named Consist Sparse Representation (CSR) to solve the problem of video-based face recognition. We treat face images from each set as an ensemble. For each probe set, our goal is that the non-zero elements of the coefficient matrix can ideally focus on the gallery examples from a few/one subject(s). To obtain the sparse representation of a probe set, we simultaneously consider group-sparsity of gallery sets and probe sets. A new matrix norm (i.e. l(F,0)-mixed norm) is designed to describe the number of gallery sets selected to represent the probe set. The coefficient matrix is obtained by minimizing the l(F,0)-mixed norm which directly counts the number of gallery sets used to represent the probe set. It could better characterize the relations among classes than previous methods based on sparse representation. Meanwhile, a special alternating optimization strategy based on the idea of introducing auxiliary variables is adopted to solve the discontinuous optimization problem. We conduct extensive experiments on Honda, COX and some image set databases. The results demonstrate that our method is more competitive than those state-of-the-art video-based face recognition methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-54187-7_27 | COMPUTER VISION - ACCV 2016, PT III |
Field | DocType | Volume |
Facial recognition system,Coefficient matrix,Pattern recognition,Computer science,Sparse approximation,Matrix norm,Auxiliary variables,Artificial intelligence,Optimization problem | Conference | 10113 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiuping Liu | 1 | 156 | 18.74 |
| Aihong Shen | 2 | 0 | 0.34 |
| Jie Zhang | 3 | 112 | 7.99 |
| Junjie Cao | 4 | 212 | 18.07 |
| Yanfang Zhou | 5 | 0 | 0.34 |