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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Wei Li Qiuqi Ruan Jun Wan |
| Copyright Year | 2010 |
| Description | Author affiliation: Institute of Information Science, Beijing Jiaotong University, Beijing, P.R. China (Wei Li; Qiuqi Ruan; Jun Wan) |
| Abstract | The uncorrelated discriminant linear analysis (ULDA) has been proved to be an effective feature extraction method and is known as a development of classical linear discriminant analysis (LDA). In real-world applications, we often encounter the "small sample size" (SSS) problem that the number of training samples is less than the dimension of feature vectors. Under this situation, the within-class scatter matrix is always singular, making the direct implementation of the ULDA algorithm inapplicable. To tackle this problem, it is common to apply a preprocessing step that transforms the data to a lower dimensional space with loss of valuable information contains in original data. In this paper, a new technique called two-dimensional uncorrelated linear discriminant analysis (2D-ULDA) is developed for solving the SSS problem. The main ingredient is the small size of covariance matrix which is suitable for the SSS problem. To evaluate the performance of the proposed 2D-ULDA, a series of experiments were performed on JAFFE database. The recognition accuracy across all experiments was higher using 2D-ULDA than ULDA. The comparison experiments of the proposed 2D-ULDA, 2DPCA and 2DFLD also demonstrated the competitiveness of our approach. |
| Starting Page | 1362 |
| Ending Page | 1365 |
| File Size | 198620 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424458974 |
| e-ISBN | 9781424459001 |
| DOI | 10.1109/ICOSP.2010.5656885 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-10-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Feature extraction Face recognition Accuracy Databases Algorithm design and analysis Linear discriminant analysis Support vector machine classification Facial expression recognition Uncorrelated Discriminant Analysis Uncorrelated space Optimal projection vectors |
| Content Type | Text |
| Resource Type | Article |
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