Face recognition using kernel principal component analysis

By Kwang In Kim, Keechul Jung, Hang-Joon Kim. IEEE Signal Processing Letters, 2002

340 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/df1ed00235072732c7b15895711434fd9476a4b6

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

1. Li Bai
2. Amit Kumar Mishra
3. LinLin Shen
4. Michael C. Fairhurst
5. Tshiamo Motaung
6. Lior Wolf

In Media Technology

1. Li Bai
2. Amit Kumar Mishra
3. LinLin Shen
4. Michael C. Fairhurst
5. Tshiamo Motaung

Citations in the corpus

• Application of linear and nonlinear PCA to SAR ATR (2015, methodology): “The kernel maps the observations into a feature space of dimensionality O(m)[11].”
• Gabor wavelets and General Discriminant Analysis for face identification and verification (2007, methodology): “Kernel based methods [9], exemplified by Kernel Principal Component Analysis (KPCA) [10,11], Kernel Fisher Discriminant Analysis (KFDA) [12,13] and General Discriminant Analysis (GDA) [14] have significantly outperformed PCA, LDA, ICA and neural networks in similar recognition tasks.”