Name
Abstract | ||
|---|---|---|
This paper proposes a novel and powerful pattern recognition method named symmetrical singular value decomposition representation (SSVDR) and presents its application to face recognition. The SSVDR method is based on singular value decomposition (SVD) and symmetry prior. In this method, the given image is firstly decomposed into a composition of a set of base images by the singular value decomposition technique. Then, the first few base images (which can be proved to be the low-frequency asymmetrical base images) are turned into symmetrical base images according to facial symmetry. Finally, a new representation of the original image is reestablished for the final recognition. For evaluating the performance of the SSVDR method, some experiments are conducted in two famous face databases: extended Yale B and CMU-PIE database. The experiment results show the proposed SSVDR method can reestablish a new homogeneous representation of the original image and has an encouraging performance on face recognition compared with the current state-of-the-art methods. A new method based on singular value decomposition (SVD) and symmetry prior for face recognition is proposed.More homogeneous image representation of the original image can be reestablished by our method.The non-uniformity is only deflated on the lower-frequency components of the original image.A significantly experiment performance compared with the current state-of-the-art methods. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2016.05.075 | Neurocomputing |
Keywords | Field | DocType |
Pattern recognition,Face recognition,Face representation,Singular value decomposition (SVD),Symmetry prior | Facial recognition system,Computer vision,Singular value decomposition,Pattern recognition,Homogeneous,Image representation,Artificial intelligence,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
214 | C | 0925-2312 |
Citations | PageRank | References |
4 | 0.39 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuhui Chen | 1 | 13 | 4.26 |
| Shuiguang Tong | 2 | 4 | 0.39 |
| Feiyun Cong | 3 | 4 | 0.39 |
| Jian Xu | 4 | 21 | 10.93 |