Face recognition using LDA mixture model
SCIE
SCOPUS
- Title
- Face recognition using LDA mixture model
- Authors
- Kim, HC; Kim, D; Bang, SY
- Date Issued
- 2003-11
- Publisher
- ELSEVIER SCIENCE BV
- Abstract
- Linear discriminant analysis (LDA) provides the projection that discriminates data well, and shows a good performance for face recognition. However, since LDA provides only one transformation matrix over the whole data, it is not sufficient to discriminate complex data consisting of many classes with high variations, such as human faces. To overcome this weakness, we propose a new face recognition method based on the LDA mixture model, where the set of all classes are partitioned into several clusters and we obtain a transformation matrix for each cluster. This accurate and detailed representation will improve classification performance. Simulation results of face recognition show that LDA mixture model outperforms PCA, LDA, and PCA mixture model in terms of classification performance. (C) 2003 Elsevier B.V. All rights reserved.
- Keywords
- linear discriminant analysis; LDA mixture model; PCA mixture model; face recognition; EM ALGORITHM
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/18381
- DOI
- 10.1016/S0167-8655(03)00126-0
- ISSN
- 0167-8655
- Article Type
- Article
- Citation
- PATTERN RECOGNITION LETTERS, vol. 24, no. 15, page. 2815 - 2821, 2003-11
- Files in This Item:
- There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.