Name
Abstract | ||
|---|---|---|
Based on non-linear Volterra kernels mapping and direct discrimination analysis(DD-Volterra), a novel face recognition algorithm is proposed. Firstly, the original image is segmented into specific sub blocks and seeks functional mapping using truncated Volterra kernels. Next, simultaneous diagonalization obtain Volterra kernel optimal projection matrix. This matrix can discard useless information that exist in the null space of the inter-class. Also, it can reserve discriminative information that exist in the null space of the intra-class. Finally, in the test, each block of the test image is classified separately, voting strategy and nearest neighbor classifier algorithm are used for classification. Experiments show that the proposed DD-Volterra method has better performance for it is more effective than Volterrafaces during the extracting facial feature stage. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-981-10-3002-4_34 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Face recognition,Feature extraction,Volterra kernels,Direct discriminant analysis | Kernel (linear algebra),Facial recognition system,Pattern recognition,Computer science,Multiple discriminant analysis,Projection (linear algebra),Feature extraction,Artificial intelligence,Linear discriminant analysis,Discriminative model,Standard test image | Conference |
Volume | ISSN | Citations |
662 | 1865-0929 | 0 |
PageRank | References | Authors |
0.34 | 13 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guang Feng | 1 | 0 | 0.68 |
| Hengjian Li | 2 | 4 | 3.10 |
| Jiwen Dong | 3 | 5 | 5.18 |
| Jiashu Zhang | 4 | 1122 | 75.03 |