Name
Abstract | ||
|---|---|---|
Sparse representation is a new hot technique in recent years. The two-phase test sample sparse representation method (TPTSSR) achieved an excellent performance in face recognition. In this paper, a kernel two-phase test sample sparse representation method (KTPTSSR) is proposed. Firstly, the input data are mapped into an implicit high-dimensional feature space by a non-linear mapping function. Secondly, the data are analyzed by means of the TPTSSR method in the feature space. If an appropriate kernel function and the corresponding kernel parameter are selected, a test sample can be accurately represented as the linear combination of the training data with the same label information of the test sample. Therefore, the proposed method could have better recognition performance than TPTSSR. Experiments on the face databases demonstrate the effectiveness of our methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1142/S0218001416560012 | INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE |
Keywords | Field | DocType |
Face recognition, sparse representation, kernel two-phase test sample sparse representation method, kernel trick | Feature vector,Radial basis function kernel,Pattern recognition,Kernel embedding of distributions,Sparse approximation,Kernel principal component analysis,Artificial intelligence,Kernel method,Variable kernel density estimation,Mathematics,Machine learning,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
30 | 1 | 0218-0014 |
Citations | PageRank | References |
0 | 0.34 | 23 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghua Liu | 1 | 115 | 11.12 |
| Jiexin Pu | 2 | 92 | 19.85 |
| Yong Qiu | 3 | 24 | 1.66 |
| Moli Zhang | 4 | 4 | 1.40 |
| Xiaoli Zhang | 5 | 0 | 0.34 |
| Guangjun Huang | 6 | 0 | 0.34 |