Name
Abstract | ||
|---|---|---|
A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-26763-6_19 | International Conference on Intelligent Computing |
DocType | Volume | Citations |
Journal | abs/1905.06458 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiao Chen | 1 | 0 | 0.34 |
| Zhigang Jia | 2 | 43 | 9.02 |
| Yunfeng Cai | 3 | 0 | 1.01 |
| Meixiang Zhao | 4 | 17 | 3.69 |