Robust Sparse 2DPCA and Its Application to Face Recognition
| Autor: | Jicheng Meng, Xiaolong Zheng |
|---|---|
| Rok vydání: | 2012 |
| Předmět: |
Computer science
business.industry Pattern recognition Machine learning computer.software_genre Facial recognition system Object-class detection Eigenface Robustness (computer science) Principal component analysis Outlier Three-dimensional face recognition Artificial intelligence Face detection business computer |
| Zdroj: | 2012 Symposium on Photonics and Optoelectronics. |
| DOI: | 10.1109/sopo.2012.6270566 |
| Popis: | This paper proposes robust sparse 2DPCA (RS2DPCA) that makes the best of semantic, structural information and suppresses outliers. The proposed RS2DPCA combines the advantages of sparsity, 2D data format and L1-norm. To assess the performance of RS2DPCA in face recognition, experiments are performed on two famous face databases, i.e. Yale, and FERET, and the experimental results indicate that the proposed RS2DPCA outperform the same class of algorithms, such as RSPCA, 2DPCAL1. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|