Abstract
This paper develops an unsupervised discriminant projection (UDP) technique for feature extraction. UDP takes the local and non-local information into account, seeking to find a projection that maximizes the non-local scatter and minimizes the local scatter simultaneously. This characteristic makes UDP more intuitive and more powerful than the up-to-date method - Locality preserving projection (LPP, which considers the local information only) for classification tasks. The proposed method is applied to face biometrics and examined using the ORL and FERET face image databases. Our experimental results show that UDP consistently outperforms LPP, PCA, and LDA.
Original language | English |
---|---|
Title of host publication | Proceedings - 18th International Conference on Pattern Recognition, ICPR 2006 |
Pages | 904-907 |
Number of pages | 4 |
Volume | 1 |
DOIs | |
Publication status | Published - 1 Dec 2006 |
Event | 18th International Conference on Pattern Recognition, ICPR 2006 - Hong Kong, Hong Kong Duration: 20 Aug 2006 → 24 Aug 2006 |
Conference
Conference | 18th International Conference on Pattern Recognition, ICPR 2006 |
---|---|
Country/Territory | Hong Kong |
City | Hong Kong |
Period | 20/08/06 → 24/08/06 |
ASJC Scopus subject areas
- Computer Vision and Pattern Recognition