Comments on "Globally Maximizing, Locally Minimizing: Unsupervised Discriminant Projection with Application to Face and Palm Biometrics"
By Weihong Deng, Jiani Hu, Jun Guo, Honggang Zhang, Chuang Zhang. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007
387 citations recorded by the source corpus.
Subfields: Computer Vision and Pattern Recognition, Signal Processing
View Paper: https://www.semanticscholar.org/paper/6fd5aeb29448e86e6ee5c3b976227f4456f68933
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Vision and Pattern Recognition
- David Dapeng Zhang
- Lei Zhang
- Xilin Chen
- Luc Van Gool
- Shiguang Shan
- Ruiping Wang
- Mohamed S. Kamel
- Dacheng Tao
In Signal Processing
- David Dapeng Zhang
- Lei Zhang
- Mohamed S. Kamel
- Adams Wai-Kin Kong
- Meng Yang
- Xilin Chen
- Ruiping Wang
- Shiguang Shan
Citations in the corpus
- Manifold Discriminant Analysis (2009, methodology): “As noted in [ 30 ], MFA and LDE are essentially the same, and can be seen as localized and nonparametric variants of LDA.”
- Gabor Feature Based Sparse Representation for Face Recognition with Gabor Occlusion Dictionary (2010, methodology): “The manifold learning methods have been proposed to overcome this limitation [7], [8], and the representative manifold learning methods include locality preserving projection (LPP) [9], local discriminant embedding (LDE) [10], unsupervised discriminant projection (UDP) [ 11 ], etc.”
- A survey of palmprint recognition (2009, background): “Some researchers have developed new subspace approaches and examined them on palmprints [71-73, 102, 105].”
- Fusing Local Patterns of Gabor Magnitude and Phase for Face Recognition (2010, methodology): “Considering the essential nonlinearity of face images, many manifold-related methods have been proposed for feature extraction, such as Isomap [34], locally linear embedding (LLE) [26], locality preserving projections (LPP) [10] and some variants of it (e.g., [4] and [ 41 ]).”
- Metaface learning for sparse representation based face recognition (2010, methodology): “Therefore, subspace learning and manifold learning methods have been dominantly and successfully used in appearance based FR [1-8], which includes Eigenface, Fisherface [1-3], locality preserving projection (LPP) [6], local discriminant embedding (LDE) [7], unsupervised discriminant projection (UDP)”