Gabor-based kernel PCA with fractional power polynomial models for face recognition

By Chengjun Liu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004

642 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Signal Processing

View Paper: https://www.semanticscholar.org/paper/dfccc4afba3fcda94ff3bb26a53d4429a596eae2

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Citations in the corpus

A review on Gabor wavelets for face recognition (2006, methodology): “All of the recognition rates listed in Table 1 are for frontal view faces only, the results for half profile and profile faces from FERET database can be found in [30 ]a nd [ 68 ].”
Optimal Local Basis: A Reinforcement Learning Approach for Face Recognition (2009, methodology): “2006), kernel machines (Kim et al. 2002; Liu 2004; Lu et al. 2003) and hybrid methods (Yang et al.”
Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions (2007, methodology): “We emphasize that it includes a number of elements that improve recognition in the face of complex lighting variations: 1) we use a combination of complementary visual features—LBP and Gabor wavelets; 2) the features are individually both robust and information-rich; 3) preprocessing—which is usuall”
Eigenfeature Regularization and Extraction in Face Recognition (2008, background): “…properties of the linear subspace analysis and its promising performance achieved in the face recognition encourage researchers to extend it to higher order statistics [10], [11], nonlinear methods [12], [13], [14], Gabor features [15], [16], and locality preserving projections [17], [18].”
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition (2007, methodology): “3 is similar to the face representation method [26], [25], which is also Gabor functions.”
Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification (2006, background): “In many cases, only the percentage of correctly identified faces wit hin a number of matches is adequate (recognition rate) for evaluating the performance of a face recognition system [1].”
Face recognition across pose: A review (2009, methodology): “Kernel tricks [36, 54, 66, 80, 82] Nonlinear transformation encapsulated in dimension reduction, simple, fast The existing kernel functions are not specific to pose variations, the choice of kernel functions are limited.”
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.”
2D and 3D face recognition: A survey (2007, background): “On the contrary, a quite different approach has been proposed by Liu (2004) (Fig.”