Incremental complete LDA for face recognition
- 作者：Gui-Fu Lu ; luguifu_jsj@163.com ; [Author Vitae] ; Jian Zou ; [Author Vitae] ; Yong Wang ; [Author Vitae]
- 关键词：Face recognition ; Feature extraction ; Dimensionality reduction ; Small sample size problem ; Incremental learning
- 刊名：Pattern Recognition
- 出版年：2012
- 期刊代码：100_00313203
- 类别：cp
- 出版时间：July, 2012
- 卷：45
- 期：7
- 页码：2510-2521
- 文件大小：1319 K
摘要
The complete linear discriminant analysis (CLDA) algorithm has been proven to be an effective tool for face recognition. The CLDA method can make full use of the discriminant information of the training samples. However, the original implementation of CLDA may not suitable for incremental learning problem. In this paper, we first propose a new implementation of CLDA, which is theoretically equivalent to the original implementation of CLDA but is more efficient than the original one. Then, based on our proposed novel implementation of CLDA, we propose the incremental CLDA method which can accurately update the discriminant vectors of CLDA when new samples are inserted into the training set. Experiments on ORL, AR and PIE face databases show the efficiency of our proposed CLDA algorithms over the original implementation of CLDA.