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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Deng Cai Xiaofei He Yuxiao Hu Jiawei Han Huang, T. |
| Copyright Year | 2007 |
| Description | Author affiliation: Illinois Univ., Urbana-Champaign (Deng Cai) |
| Abstract | Subspace learning based face recognition methods have attracted considerable interests in recently years, including principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE), marginal fisher analysis (MFA) and local discriminant embedding (LDE). These methods consider an $n_{1}timesn_{2}$ image as a vector in $R^{n}$ $_{1}$ $^{timesn}$ $_{2}$ and the pixels of each image are considered as independent. While an image represented in the plane is intrinsically a matrix. The pixels spatially close to each other may be correlated. Even though we have $n_{1}xn_{2}$ pixels per image, this spatial correlation suggests the real number of freedom is far less. In this paper, we introduce a regularized subspace learning model using a Laplacian penalty to constrain the coefficients to be spatially smooth. All these existing subspace learning algorithms can fit into this model and produce a spatially smooth subspace which is better for image representation than their original version. Recognition, clustering and retrieval can be then performed in the image subspace. Experimental results on face recognition demonstrate the effectiveness of our method. |
| Starting Page | 1 |
| Ending Page | 7 |
| File Size | 283597 |
| Page Count | 7 |
| File Format | |
| ISBN | 1424411793 |
| ISSN | 10636919 |
| DOI | 10.1109/CVPR.2007.383054 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2007-06-17 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Face recognition Principal component analysis Linear discriminant analysis Pixel Vectors Laplace equations Subspace constraints Clustering algorithms Image representation Image recognition |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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