Name
Abstract | ||
|---|---|---|
Two-dimensional principal component analysis (2DPCA) proposed recently overcome a limitation of principal component analysis (PCA) which is expensive computational cost. Symmetrical principal component analysis (SPCA) is also a better feature extraction technique because it utilizes effectively the symmetrical property of human face. This paper presents a symmetry based two-dimensional principal component analysis (S2DPCA), which combines the advantages of 2DPCA and of the SPCA. The experimental results show that S2DPCA is competitive with or superior to 2DPCA and SPCA. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72393-6_124 | ISNN (2) |
Keywords | Field | DocType |
principal component analysis,face recognition,human face,expensive computational cost,symmetrical principal component analysis,two-dimensional principal,two-dimensional principal component analysis,symmetrical property,component analysis,better feature extraction technique,feature extraction | Facial recognition system,Pattern recognition,Computer science,Feature extraction,Robust principal component analysis,Kernel principal component analysis,Artificial intelligence,Eigendecomposition of a matrix,Principal component analysis,Machine learning | Conference |
Volume | ISSN | Citations |
4492 | 0302-9743 | 2 |
PageRank | References | Authors |
0.38 | 7 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mingyong Ding | 1 | 2 | 0.38 |
| Congde Lu | 2 | 14 | 2.72 |
| Yunsong Lin | 3 | 2 | 0.38 |
| Ling Tong | 4 | 4 | 4.40 |